Furnace temperature prediction method, furnace temperature control method, furnace temperature prediction apparatus, and furnace temperature control apparatus

Abstract

This furnace temperature prediction method is executed by a furnace temperature prediction apparatus that predicts, using a temperature prediction model, the temperature of a combustion chamber or carbonization chamber in a coke oven in which carbonization chambers (3) and combustion chambers (2) each having, in a lower part thereof, a heat storage chamber are alternately connected to constitute a furnace group. The furnace temperature prediction method involves: acquiring operation record information including information on coal charging into a carbonization chamber, information relating to coal to be charged, information on fuel to be supplied to a combustion chamber, a target temperature of the combustion chamber or carbonization chamber, and a temperature of the combustion chamber or carbonization chamber at the time of prediction; and predicting, using the temperature prediction model for each of the plurality of combustion chambers or the plurality of carbonization chambers, the temperature of each of the plurality of combustion chambers or the plurality of carbonization chambers after a predetermined time on the basis of the operation record information.

Description

Furnace temperature prediction method, furnace temperature control method, furnace temperature prediction device, and furnace temperature control device.

[0001] This disclosure relates to a furnace temperature prediction method, a furnace temperature control method, a furnace temperature prediction device, and a furnace temperature control device. In particular, this disclosure relates to a furnace temperature prediction method, a furnace temperature control method, a furnace temperature prediction device, and a furnace temperature control device in a coke oven in which combustion chambers and carbonization chambers are alternately connected to form a furnace group.

[0002] In a coke oven, which consists of multiple combustion chambers and carbonization chambers connected alternately to form a furnace group, coke is produced by carbonizing coal charged into the carbonization chamber using heat from the adjacent combustion chamber. To stabilize the quality of coke produced in a coke oven, improve production efficiency, and reduce the amount of heat required for carbonization, it is important to control the temperature of the combustion chamber or carbonization chamber to the desired temperature. Because coke ovens have a large heat capacity and a long time constant for response to actions to manipulate the furnace temperature, it is necessary to predict future temperatures with high accuracy and take action accordingly in order to control the temperature of the combustion chamber or carbonization chamber with high precision. However, the temperature of a coke oven fluctuates in a complex manner due to numerous fluctuating factors, including the fuel gas supply conditions, the properties of the coal being charged, the carbonization conditions, and the conditions of the adjacent carbonization and combustion chambers. Therefore, it has been difficult to predict future temperatures with high precision.

[0003] As a method for predicting future temperatures, temperature prediction techniques using statistical methods have been developed. For example, Patent Document 1 uses past performance data to calculate target furnace group temperatures for multiple future time periods based on an evaluation function that includes a term representing the difference between the coke temperature predicted using a coke temperature prediction model and the target temperature.

[0004] Japanese Patent Publication No. 2023-39670

[0005] In coke oven operation, the control parameters are adjusted to minimize temperature fluctuations. Statistical methods based on historical data often fail to accurately predict oven temperature because the correlation between temperature fluctuations and control parameters is difficult to discern. However, methods using physical calculations (methods employing physical models) can provide more accurate predictions. Generally, using physical models involves a high computational load, making frequent calculations difficult and thus unsuitable for controlling actual coke ovens.

[0006] In view of these circumstances, the purpose of this disclosure is to provide a furnace temperature prediction method, a furnace temperature control method, a furnace temperature prediction device, and a furnace temperature control device that enable highly accurate furnace temperature prediction using a physical model suitable for control in actual equipment.

[0007] (1) A furnace temperature prediction method according to one embodiment of the present disclosure is a furnace temperature prediction method performed by a furnace temperature prediction device that predicts the temperature of a combustion chamber or a carbonization chamber in a coke oven in which a combustion chamber having a heat storage chamber at the bottom and a carbonization chamber are alternately connected to form a furnace group, the method comprising: the temperature prediction model being a physical model capable of transient calculations that performs temperature prediction calculations for the entire furnace group including the combustion chamber, the carbonization chamber and the heat storage chamber; acquiring operational performance information including information on coal charging to the carbonization chamber, information related to the coal to be charged, information on fuel supplied to the combustion chamber, a target temperature for the combustion chamber or the carbonization chamber, and the temperature of the combustion chamber or the carbonization chamber at the time of prediction; and predicting the temperature of each of the multiple combustion chambers or multiple carbonization chambers after a predetermined time based on the operational performance information, using the respective temperature prediction models for each of the multiple combustion chambers or multiple carbonization chambers.

[0008] (2) In one embodiment of the present disclosure, in (1), the temperature prediction model is a physical model constructed by constructing the heat transfer in all the connected combustion chambers and carbonization chambers as a horizontal one-dimensional heat transfer model, the heat transfer in all the heat storage chambers as a two-dimensional heat transfer model, and the temperature at each temperature measurement position in all the combustion chambers or all the carbonization chambers as a vertical one-dimensional heat transfer model from each of the combustion chambers or all the carbonization chambers.

[0009] (3) In one embodiment of the present disclosure, in (2), the vertical one-dimensional heat transfer model calculates the vertical heat transfer including the bricks from the center of each combustion chamber or carbonization chamber to the temperature measurement position at the top of the furnace, and calculates the temperature at the temperature measurement position based on the calculation results of the horizontal one-dimensional heat transfer model.

[0010] (4) In one embodiment of the present disclosure, in any of (1) to (3), the temperature prediction by the temperature prediction model is characterized by including a first calculation flow that reproduces the equipment state based on past operational performance information up to the prediction time, and a second calculation flow that predicts the temperature based on future operational information from the prediction time to a predetermined time ahead.

[0011] (5) In one embodiment of the present disclosure, in any of (1) to (4), the calculation of the combustion gas temperature of the combustion chamber in the temperature prediction model is characterized in that the combustion gas temperature is calculated such that the amount of heat generated during complete combustion, calculated based on the fuel gas components and fuel gas flow rate, is equal to the amount of heat required for the temperature rise, calculated based on the pre-combustion mixture temperature, the specific heat of each combustion gas component, and the post-combustion fuel gas flow rate.

[0012] (6) In one embodiment of the present disclosure, in (5), when the coke oven employs an interval combustion method, the amount of heat removed by the inflow of air during the period when the fuel gas to the combustion chamber is stopped is calculated and a correction is made to reflect this in the combustion gas temperature.

[0013] (7) In one embodiment of the present disclosure, in (5), when the coke oven employs an interval combustion method, the combustion gas temperature is calculated by correcting the air ratio of the fuel gas based on the ratio of the fuel gas stop period to the fuel gas supply period to the combustion chamber.

[0014] (8) In one embodiment of the present disclosure, in any of (1) to (7), the temperature prediction model is configured to allow adjustment of parameters for heat transfer, and further includes adjusting the parameters based on an error which is the difference between a calculated value and an actual value of the temperature of the combustion chamber or the carbonization chamber from a point in time a predetermined time prior to the prediction point up to the prediction point.

[0015] (9) In one embodiment of the present disclosure, the parameters of the temperature prediction model are optimized based on the error between the calculated value at the temperature measurement position calculated by a one-dimensional heat transfer model in the vertical direction and the actual temperature measurement value in (8).

[0016] (10) In one embodiment of the present disclosure, in (8) or (9), the optimization of the parameters is characterized in that the parameters of the combustion chamber are adjusted at each temperature measurement timing, and the parameters of the carbonization chamber are adjusted when the fire in each carbonization chamber is extinguished.

[0017] (11) In one embodiment of the present disclosure, in any of (1) to (10), the predetermined time is set to a time greater than the time constant of the coke oven.

[0018] (12) A furnace temperature control method according to one embodiment of the present disclosure predicts the temperature of the combustion chamber or the carbonization chamber by any of the furnace temperature prediction methods (1) to (11), and controls the supply of fuel by calculating the amount of fuel to be supplied as an operand so that the predicted temperature approaches the target temperature.

[0019] (13) As one embodiment of the present disclosure, in (12), a furnace temperature control method using the furnace temperature prediction method of (4), wherein in the second calculation flow, when multiple carbonization chambers are expected to burn out within the prediction period, the future target furnace temperature or adjustment amount for each combustion chamber adjacent to each carbonization chamber is individually determined so that the predicted burn-out time for each carbonization chamber is aligned with a common target burn-out time, and the fuel supply amount is calculated as an operating amount based on the target furnace temperature or adjustment amount and the fuel supply is controlled.

[0020] (14) A furnace temperature prediction device according to one embodiment of the present disclosure is a furnace temperature prediction device that predicts the temperature of a combustion chamber or a carbonization chamber in a coke oven in which a combustion chamber having a heat storage chamber at the bottom and a carbonization chamber are alternately connected to form a furnace group, using a temperature prediction model, wherein the temperature prediction model is a physical model capable of transient calculations that performs temperature prediction calculations for the entire furnace group including the combustion chamber, the carbonization chamber and the heat storage chamber, and includes an input device that acquires operational performance information including information on coal charging to the carbonization chamber, information related to coal to be charged, information on fuel supplied to the combustion chamber, a target temperature for the combustion chamber or the carbonization chamber, and the temperature of the combustion chamber or the carbonization chamber at the time of prediction, and a furnace temperature prediction unit that predicts the temperature of each of the multiple combustion chambers or multiple carbonization chambers after a predetermined time based on the operational performance information, using the respective temperature prediction models for the multiple combustion chambers or multiple carbonization chambers.

[0021] (15) A furnace temperature control device according to one embodiment of the present disclosure predicts the temperature of the combustion chamber or the carbonization chamber using the furnace temperature prediction device of (14), and controls the supply of fuel by calculating the amount of fuel to be supplied as an operand so that the predicted temperature approaches the target temperature.

[0022] According to this disclosure, it is possible to provide a furnace temperature prediction method, a furnace temperature control method, a furnace temperature prediction device, and a furnace temperature control device that enable highly accurate furnace temperature prediction using a physical model suitable for control in actual equipment.

[0023] Figure 1 is a schematic diagram showing the configuration of a furnace temperature prediction device according to one embodiment of the present disclosure. Figure 2 is a diagram illustrating an example of the configuration of a temperature prediction model. Figure 3 is a diagram illustrating heat transfer in a heat storage chamber. Figure 4 is a diagram illustrating a two-dimensional heat transfer model for a heat storage chamber. Figure 5 is a diagram illustrating a one-dimensional heat transfer model for a combustion chamber and a carbonization chamber. Figure 6 is a diagram illustrating a one-dimensional heat transfer model for a temperature measurement position. Figure 7 is a flowchart illustrating an example of processing for a furnace temperature prediction method according to one embodiment of the present disclosure. Figure 8 is a flowchart illustrating another example of processing for a furnace temperature prediction method according to one embodiment of the present disclosure. Figure 9 is a flowchart illustrating another example of processing for a furnace temperature prediction method according to one embodiment of the present disclosure. Figure 10 is a diagram showing an example of comparing the temperature prediction accuracy when heat transfer calculations in the vertical direction are performed and when they are not. Figure 11 is a diagram showing an example of the comparison results of the prediction accuracy of the furnace group average temperature with and without learning. Figure 12 is a diagram illustrating the interval combustion method. Figure 13 is a diagram showing an example of comparing the furnace temperature prediction accuracy of Embodiment 1 and Embodiment 2.

[0024] Hereinafter, a furnace temperature prediction method, a furnace temperature control method, a furnace temperature prediction device 20 (see Figure 1), and a furnace temperature control device according to one embodiment of the present disclosure will be described with reference to the drawings.

[0025] As described above, in order to stabilize the quality of coke produced in a coke oven, improve production efficiency, and reduce the amount of heat required for carbonization, it is important to control the temperature of the combustion chamber 2 (see Figure 1) or carbonization chamber 3 (see Figure 1) to the desired temperature. Because coke ovens have a large heat capacity and a long time constant for response to actions to control the oven temperature, it is necessary to predict future temperatures with high accuracy and take action accordingly. As a method for predicting future temperatures, statistical methods based on actual data, for example, do not easily show a correlation between temperature fluctuations and operational items, limiting the accuracy of oven temperature prediction. Here, methods using physical models can make accurate predictions. However, conventional methods using physical models generally have a high computational load, making frequent calculations difficult and unsuitable for controlling actual coke oven operations. In other words, a coke oven is a massive piece of equipment, and representing all of it as a three-dimensional spatial phenomenon in a physical model results in a high computational load. Therefore, it is difficult to predict and control the oven temperature in real time in an actual machine.

[0026] One method to reduce the computational load of a physical model is to simulate phenomena in three-dimensional space in one or two dimensions. However, generally, the computational load and accuracy of a physical model are inversely proportional, and this can lead to a decrease in calculation accuracy. Therefore, it is preferable to use a physical model that simulates the coke oven as much as possible in one-dimensional space, and partially employs simulation in two-dimensional space to avoid a decrease in calculation accuracy. When simulating a coke oven in one-dimensional space, only phenomena in one of the following directions are simulated: the extrusion direction, the vertical direction, and the horizontal direction perpendicular to the extrusion direction. Here, the extrusion direction is the direction in which the coke is pushed out of the coke oven. The vertical direction is the up and down direction perpendicular to the extrusion direction, and corresponds to the stacking direction of the combustion chamber 2 and the heat storage chamber in Figure 1. The horizontal direction is the left and right direction perpendicular to the extrusion direction and the vertical direction, and corresponds to the connecting direction of the combustion chamber 2 and the carbonization chamber 3 in Figure 1. For example, if the phenomena in the horizontal direction are simulated with a one-dimensional model, the influence of the vertical and extrusion directions cannot be considered, resulting in a decrease in calculation accuracy. Therefore, by performing one-dimensional or two-dimensional calculations that include directions other than the horizontal only in the parts where the computational load is relatively small and which greatly contribute to the calculation accuracy, it becomes possible to construct a physical model with a low computational load and high calculation accuracy. An example of the configuration of the temperature prediction model in this embodiment (Figure 2) will be described later.

[0027] (Device Configuration) Figure 1 is a schematic diagram showing the configuration of the furnace temperature prediction device 20 according to this embodiment. The coke oven shown in Figure 1 comprises N combustion chambers 2 (2-1 to 2-N) and N-1 carbonization chambers 3 (3-1 to 3-(N-1)). Each of the N combustion chambers 2 has a heat storage chamber at its lower part (downward in the vertical direction, which is the fuel supply side). The coke oven is composed of a furnace group in which combustion chambers 2 having a heat storage chamber at the lower part and carbonization chambers 3 are alternately connected. Here, N is an integer of 2 or more, but is not limited to a specific number. The coke oven charges coal, which is the raw material, into the carbonization chamber 3 and supplies fuel gas (G in Figure 1) to the combustion chamber 2. The coke oven produces coke by carbonizing the coal in the carbonization chamber 3 by heating the carbonization chamber 3 with the heat generated by the combustion chambers 2 on both sides. The furnace temperature prediction device 20 according to this embodiment predicts the temperature of the combustion chamber 2 or the carbonization chamber 3 in the coke oven using a temperature prediction model described later. Here, in controlling the furnace temperature of the coke oven as described later, it is sufficient if the temperature of either the combustion chamber 2 or the carbonization chamber 3 is predicted. In this embodiment, it is explained that the temperature of the combustion chamber 2 is predicted, but as an alternative example, the temperature of the carbonization chamber 3 may be predicted.

[0028] The coke oven is equipped with a main gas pipe 4, one end of which is connected to a gas supply source. The other ends of the N-1 branches of the main gas pipe 4 are piped to the combustion chambers 2, supplying fuel gas to the combustion chambers 2. One end of the main gas pipe 4 is provided with a control valve 5 (furnace gas cock) for adjusting the flow rate of fuel gas supplied to the entire furnace group (the total flow rate of fuel gas supplied to the combustion chambers 2). In addition, each of the other branches is provided with individual furnace control valves 6 (6-1 to 6-(N-1)) for fine-tuning the distributed gas flow rate before supplying it to the combustion chambers 2. The fuel gas fine-tuned by the 1st to (N-1)th individual furnace control valves 6 (individual furnace gas cocks) may be supplied to the two combustion chambers 2 adjacent to the corresponding 1st to (N-1)th carbonization chambers 3. The opening degree (control valve opening degree) of the control valves 5 and individual furnace control valves 6 is controlled by a control terminal 10. The control terminal 10 functions as a furnace temperature control device, which will be described later.

[0029] The furnace temperature prediction system is mainly composed of a control terminal 10, a furnace temperature prediction device 20, and a display device 30. The control terminal 10 is composed of an information processing device such as a personal computer or a workstation. The control terminal 10 may not be an independent device, but may be composed of a part of a computer that functions as the control terminal 10 and the furnace temperature prediction device 20. The control terminal 10 monitors the states of the combustion chamber 2 and the carbonization chamber 3, and manages the combustion chamber 2 and the carbonization chamber 3 according to the furnace temperature prediction device 20. In addition, the control terminal 10 manages the operation of the coke oven. For example, the control terminal 10 adjusts the opening degree of the regulating valve 5 of the regulating valve, and controls the flow rate of the fuel gas supplied to the entire furnace group so that the average temperature value of the combustion chamber 2 and the carbonization chamber 3 of the entire furnace group reaches the target temperature. In addition, the control terminal 10 adjusts the opening degree of the regulating valve of the regulating valve 6 for each kiln to control the flow rate of the fuel gas supplied to the combustion chamber 2, thereby managing the operation of the coke oven so that the temperatures in the combustion chamber 2 and the carbonization chamber 3 respectively reach the individually set target temperatures. In addition, the control terminal 10 may manage an operation schedule obtained from a higher-level computer or input by an operator.

[0030] The furnace temperature prediction device 20 is composed of an information processing device such as a personal computer or a workstation. The furnace temperature prediction device 20 includes an input device 21, a temperature calculation unit 24, a heat transfer calculation unit 23, a furnace temperature prediction unit 25, and an output device 26.

[0031] The input device 21, the heat transfer calculation unit 23, the temperature calculation unit 24, the furnace temperature prediction unit 25, and the output device 26 can be realized by an arithmetic processing device such as a CPU. The input device 21, the heat transfer calculation unit 23, the temperature calculation unit 24, the furnace temperature prediction unit 25, and the output device 26 are realized, for example, by executing a computer program in an arithmetic processing device. Here, as another example, the input device 21, the heat transfer calculation unit 23, the temperature calculation unit 24, the furnace temperature prediction unit 25, and the output device 26 may have a dedicated arithmetic device or arithmetic circuit.

[0032] The input device 21 is an input interface to which various measurement results and operation performance information related to the coke oven are input. The input device 21 may be configured to include a keyboard, a mouse, a pointing device, a data receiving device, a graphical user interface (GUI), and the like. The input device 21 receives operation performance information and parameter setting values from the outside and transmits them to the temperature calculation unit 24.

[0033] Operation performance information is input to the input device 21 from the control terminal 10. The operation performance information includes information acquired from various measuring devices (sensors). For example, the combustion chamber temperature is a measured value by a thermocouple or a radiation thermometer. The fuel gas component is a measured value by a gas component meter or an infrared sensor. The fuel gas flow rate is a measured value by a sensor such as a differential pressure type flow meter. The air volume is a value calculated from the exhaust gas O ２ concentration. The amount of coal charged, the moisture content of the coal, the volatile component amount, the particle size, etc. are sensor information acquired from a coal charging car or an analyzer, etc. Based on these sensor information, the operation performance information is constituted. The operation performance information may include density, specific heat, thermal conductivity, and the coke opening degree for each kiln changed manually.

[0034] In addition, the operation performance information may include an operation schedule (that is, an extrusion and coal charging schedule) and physical property values of bricks constituting the coke oven. The physical property values of the bricks may include the density, specific heat, thermal conductivity, etc. of the bricks. For example, the operation performance information includes coal charging information to the carbonization chamber 3, coal-related information to be charged, fuel information supplied to the combustion chamber 2, the target temperature of the combustion chamber 2 or the carbonization chamber 3, and the temperature of the combustion chamber 2 or the carbonization chamber 3 at the time of prediction. In this embodiment, since the temperature of the combustion chamber 2 is predicted as described above, the operation performance information includes the target temperature of the combustion chamber 2 and the temperature of the combustion chamber 2 at the time of prediction. Also, for example, the operation performance information includes the temperatures of the fuel gas and air introduced into the regenerator.

[0035] The temperature calculation unit 24 takes the fuel gas components, furnace group air volume, furnace group fuel gas flow rate, fuel gas flow rate per kiln, air volume per kiln, and pre-combustion mixture temperature as input values ​​and calculates the combustion gas temperature, combustion gas flow rate, combustion gas components, specific heat, density, thermal conductivity of the combustion gas, and heat transfer coefficient in the combustion chamber 2. Here, the fuel gas flow rate per kiln may be calculated based on the opening of the cock for each kiln. The air volume per kiln may be calculated from the air valve for each kiln. The pre-combustion mixture temperature may be measured or calculated.

[0036] The heat transfer calculation unit 23 uses the values ​​calculated by the temperature calculation unit 24 and the values ​​obtained from the input device 21 to calculate the temperature, coal moisture content, and volatile component content at each location in the coke oven (calculation points such as bricks, coal, and combustion chamber 2). Here, the values ​​calculated by the temperature calculation unit 24 include combustion gas temperature, combustion gas flow rate, combustion gas components, specific heat, density, thermal conductivity of the combustion gas, and heat transfer coefficient in the combustion chamber 2. The values ​​obtained from the input device 21 include coal charge amount, coal moisture content, volatile component content, particle size, coal density, specific heat, thermal conductivity, operation schedule, and physical properties of the bricks constituting the coke oven.

[0037] The furnace temperature prediction unit 25 takes planned values, etc., and the temperature, coal moisture content, and volatile component content at each position of the coke oven calculated by the heat transfer calculation unit 23 as input values ​​to calculate (predict) the future temperature, coal moisture content, and volatile component content at each position of the coke oven. Planned values, etc., are future planned values ​​or the latest values ​​of fuel gas components, fuel gas flow rate, air amount, kiln cock opening degree, charge amount, coal moisture content, volatile component content, particle size, coal density, specific heat, thermal conductivity, operation schedule, and physical properties of the bricks constituting the coke oven. Planned values, etc., can be obtained as part of the operational performance information. Based on the operational performance information, the furnace temperature prediction unit 25 predicts the temperature of each of the multiple combustion chambers 2 or multiple carbonization chambers 3 after a predetermined time using the respective temperature prediction models for the multiple combustion chambers 2 or multiple carbonization chambers 3. In this embodiment, since the temperature of the combustion chamber 2 is predicted as described above, the furnace temperature prediction unit 25 predicts the temperature of each of the multiple combustion chambers 2 after a predetermined time using a temperature prediction model for each of the multiple combustion chambers 2. Here, the temperature prediction model is a physical model that performs transient calculations (calculations that divide the process into short time intervals and gradually advance the time to determine the phenomenon at the next time interval) and performs temperature prediction calculations for the entire furnace group, including the combustion chamber 2, carbonization chamber 3, and heat storage chamber.

[0038] The output device 26 outputs to the control terminal 10 the future temperature, coal moisture content, and volatile component content at each position of the coke oven calculated by the furnace temperature prediction unit 25, and the current temperature, coal moisture content, and volatile component content at each position of the coke oven at the time of prediction calculated by the heat transfer calculation unit 23.

[0039] The control terminal 10 calculates and adjusts the optimal kiln-specific cock openings using the future temperature, coal moisture content, and volatile component content at each position of the coke oven obtained from the output device 26, as well as the predicted coal moisture content, volatile component content, and temperature or measured values ​​at each position of the coke oven at the predicted time. In other words, the control terminal 10 adjusts the openings of the control valve 5 and the kiln-specific control valve 6 based on the information transmitted from the output device 26. In this way, the control terminal 10 functions as a furnace temperature control device and executes the furnace temperature control method. In the furnace temperature prediction system, the furnace temperature prediction device 20 predicts the temperature of the combustion chamber 2 or carbonization chamber 3, and the furnace temperature control device calculates the fuel supply amount as an operand and controls the fuel supply so that the predicted temperature approaches the target temperature. In the coke production method in a coke oven, the control terminal 10, which is a furnace temperature control device, controls the fuel supply (by adjusting the fuel gas supply amount or the opening of the control valve that adjusts the fuel gas supply amount) to produce coke.

[0040] Furthermore, the output device 26 also has the function of transmitting information calculated by the furnace temperature prediction device 20 to the display device 30, and it is also possible to display the calculation results output from the furnace temperature prediction device 20. At this time, the display device 30 displays the future temperature, coal moisture content, and volatile component content at each position of the coke oven obtained from the output device 26. Here, the display device 30 is, for example, a liquid crystal display.

[0041] (Furnace Temperature Prediction Method) The furnace temperature prediction device 20 having the above configuration predicts the temperature of the combustion chamber 2 or carbonization chamber 3 in the coke oven using a temperature prediction model by performing the processing of the furnace temperature prediction method described below.

[0042] Figure 7 is a flowchart showing an example of the furnace temperature prediction process (furnace temperature prediction process) performed by the furnace temperature prediction device 20 according to this embodiment. The furnace temperature prediction process may be started at regular intervals of calculation cycles, or it may be started at any time by an operator pressing a button. It is desirable that the calculation cycle be longer than the time required for one furnace temperature prediction calculation.

[0043] The furnace temperature prediction process consists of two main calculation flows, as shown in Figure 7: a first calculation flow (Pa) that reproduces the equipment state based on past operational performance information, and a second calculation flow (Pb) that predicts the temperature based on future operational information from the prediction point to a predetermined time in the future. In Pa, based on operational performance information (sensor information) that includes temperature data from a predetermined time (T) in the past (t = -T) to the prediction point (t = 0), the system calculates internal equipment state quantities that cannot be measured by sensor information. Examples of internal equipment state include the temperature distribution inside the coal loaded in the carbonization chamber 3 and the temperature distribution of the bricks in the coke oven, including the heat storage chamber. In Pb, prediction calculations are performed from the prediction point (t = 0) to a predetermined time in the future (t = T), based on operational plans corresponding to future events such as extrusion and charging of coal in the carbonization chamber 3. This enables highly accurate temperature prediction that is in line with actual operations.

[0044] Here, in a coke oven, for example, it takes about 8 hours or more for the temperature to reach a steady state by varying the gas flow rate. The predetermined time (T) should be set to be greater than the time it takes for the temperature at each position in the coke oven to reach a steady state due to such changes in operating conditions. In other words, assuming that the time constant of the coke oven is about 8 to 12 hours, the predetermined time (T) should be set to a time greater than the time constant of the coke oven, for example, to 20 hours. The temperature is calculated sequentially while gradually advancing time with a time step width Δt, and in the first calculation flow (Pa), the equipment state is reproduced using past performance information, so the initial state at the predicted time (t=0) is set with high accuracy.

[0045] In the input steps (S11 and S21), the input device 21 acquires operational performance information. The operational performance information may include combustion chamber temperature, carbonization chamber temperature, fuel gas components, fuel gas flow rate, air volume, cock opening for each furnace, coal charge amount, coal moisture content, volatile component content, particle size, coal density, specific heat, thermal conductivity, operation schedule, and physical properties of the bricks constituting the coke oven. In addition, in input step S21, operational performance information beyond the predicted time is input, and is labeled as future operational information in Figure 7. The future operational information may include fuel gas components, fuel gas flow rate, air volume, cock opening for each furnace, coal charge amount, coal moisture content, volatile component content, particle size, coal density, specific heat, thermal conductivity, operation schedule, and physical properties of the bricks constituting the coke oven. The future operational information represents the projected future values ​​of these values. If projected values ​​are not specified, the latest values ​​may be treated as continuing.

[0046] In S11, T is the predetermined time mentioned above, and the time to be predicted is set to t=0, with the calculation starting from t=-T. t represents the time in the transient calculation, and T is a number greater than or equal to 0. A time step Δt is set to be shorter than the predetermined time (T), and the transient calculation is performed by advancing time by Δt from t=-T to t=0, and from t=0 to t=T (No. in S12, S15, S22, and S25). In the first calculation flow (Pa), the combustion gas temperature is calculated (S13), the coke oven heat transfer is calculated (S14), and when t=0, the calculation ends (Yes in S15), and the second calculation flow (Pb) begins. In the second calculation flow (Pb), the combustion gas temperature is calculated (S23) and the heat transfer in the coke oven is calculated (S24) as time advances by Δt, and the calculation ends when t = T (Yes in S25).

[0047] (Calculation of Combustion Gas Temperature) The temperature calculation unit 24 calculates the combustion gas temperature using the fuel gas components, fuel gas flow rate, air volume, and pre-combustion mixture temperature. Here, the manipulated variables for furnace temperature control in this invention are the fuel gas flow rate, air volume, and gas calories (by adjusting the ratio of gas B and gas C). The temperature calculation unit 24 calculates the amount of heat generated during complete combustion of the fuel gas from the fuel gas components and fuel gas flow rate. Specifically, the amount of heat generated during complete combustion is calculated as the sum of "flow rate for each gas component × heat generated for each gas component" obtained for each gas component. The temperature calculation unit 24 first integrates the heat generated for each type of gas from the fuel gas components and flow rate, and calculates the amount of heat generated during complete combustion per unit time (E) using the following formula.

[0048]

[0049] Here, E is the calorific value of the fuel gas per unit time during complete combustion [kJ / s], the subscript m represents the type of gas contained in the fuel gas, and e is the calorific value for each type of gas [kJ / Nm]. ３ ], Q is the flow rate for each type of gas [Nm³ ３ / s] is the case.

[0050] Next, the temperature calculation unit 24 calculates the components and flow rate of the post-combustion gas (combustion gas) from the fuel gas components, fuel gas flow rate, and air quantity using a chemical reaction equation. If the air quantity is sufficient, complete combustion is assumed.

[0051] The temperature calculation unit 24 then uses the combustion gas components, flow rate (fuel gas flow rate), calorific value, and pre-combustion mixture temperature to calculate the pre-combustion mixture temperature T ｂ The combustion gas temperature is calculated such that the amount of heat consumed to raise the temperature of the combustion gas is equal to the calorific value of the fuel gas. The combustion gas temperature is calculated as a temperature T that satisfies the following equation.

[0052]

[0053] Here, T ｂ Q' is the pre-combustion mixture temperature [°C], ρ is the specific heat of each gas type (each fuel gas component), and Q' is the flow rate of each gas type after combustion.

[0054] Here, the pre-combustion mixture temperature is obtained by calculating the heat exchange between the bricks and the fluid in the heat storage chamber, but measured values ​​may be used instead of calculated values. The specific heat and density of the combustion gas are calculated based on the temperature characteristics of each gas component. The heat transfer coefficient of the gas in the combustion chamber 2 may be calculated by selecting a suitable known approximation formula from the shape of the gas pipes in the combustion chamber 2. By calculating the heat transfer coefficient in the combustion chamber 2 using a known approximation formula corresponding to the shape of the gas pipes, it becomes possible to predict the temperature that reflects the heat transfer characteristics according to the equipment structure, and a furnace temperature prediction model with high applicability to actual equipment can be constructed.

[0055] In this embodiment, the combustion gas temperature is calculated based on the fuel gas components, fuel gas flow rate, air volume, and pre-combustion mixture temperature, derived from the balance between calorific value and heat balance. This allows for stable temperature prediction even with fluctuations in combustion conditions. As a result, the combustion gas temperature fluctuations within the combustion chamber 2 can be grasped with high accuracy, contributing to an improvement in the accuracy of the furnace temperature prediction model.

[0056] Furthermore, by calculating the gas components and physical properties (specific heat, density, thermal conductivity) after combustion based on the temperature characteristics of each gas component, it becomes possible to create a physical model that closely reflects actual combustion behavior, enabling detailed prediction of thermal behavior.

[0057] (Temperature Prediction Model) Figure 2 is a diagram illustrating an example of the configuration of the temperature prediction model in this embodiment. The furnace temperature prediction device 20 predicts the temperature of the combustion chamber 2 or carbonization chamber 3 (the temperature of the combustion chamber 2 in this embodiment) in the coke oven using the temperature prediction model in Figure 2. The temperature prediction model in this embodiment simulates phenomena in the horizontal direction (the direction in which the combustion chamber 2 and carbonization chamber 3 are connected) with a one-dimensional model, and further simulates heat transfer in the vertical direction to the thermocouple (an example of a temperature measuring device) in the combustion chamber 2 with another one-dimensional model. That is, in order to predict the temperature of each of the multiple combustion chambers 2, a model combining one-dimensional models in the horizontal and vertical directions is used. By using a model that combines one-dimensional models in the horizontal and vertical directions, the computational load is reduced, and it becomes possible to improve the calculation accuracy by also considering heat transfer to the thermocouple. In this embodiment, the one-dimensional model in the horizontal direction is based on a position near the center of the combustion chamber 2 or carbonization chamber 3. The one-dimensional vertical model performs heat transfer calculations including bricks exposed to the outside air in the vertical direction from the center of each combustion chamber 2 or carbonization chamber 3 to the thermocouple at the top of the furnace. The vertical heat transfer calculations are used for data assimilation or correction of output results based on the results of the horizontal heat transfer calculations. To further improve calculation accuracy, a two-dimensional model is used for heat transfer calculations of the heat storage chamber, which is a part that greatly contributes to calculation accuracy. Below, the details of the heat transfer calculation of the heat storage chamber will be explained first. Next, the details of the model that combines the horizontal and vertical one-dimensional models will be explained.

[0058] (Heat transfer calculation in the heat storage chamber) As described above, heat transfer in all heat storage chambers is constructed as a two-dimensional heat transfer model. Here, the heat storage chamber is partitioned horizontally by bricks (see Figure 1). As shown in Figure 3, there is heat radiation from the bricks to the fuel gas and air introduced into the combustion chamber 2, and heat storage from the exhaust gas discharged from the combustion chamber 2 to the bricks. The two-dimensional model for the heat storage chamber is a model for performing heat transfer calculations by discretizing the heat storage chamber into two dimensions (see Figure 4).

[0059] In this embodiment, two dimensions are used to perform heat transfer calculations between the fluid and the bricks: the vertical direction, which is the direction of the flow path, and the horizontal direction, which is the direction in which the combustion chamber 2 and the carbonization chamber 3 are aligned. This is because, in reality, the heat storage chamber preheats the air and fuel supplied from the outside and stores heat from the exhaust gas. If the heat storage state of the heat storage chamber cannot be accurately represented, the accuracy of reproducing the time delay phenomenon of the gas temperature or the amount of heat supplied to the combustion chamber 2 will decrease.

[0060] The horizontal direction can also be considered as the extrusion direction. Furthermore, since the heat storage chamber has passages through which different gases such as air, fuel gas, and exhaust gas flow, heat transfer calculations are performed for passages through which only air flows, passages through which only fuel gas flows, and passages through which only exhaust gas flows. However, it is possible to change the flowing gas, such as changing the passage to one through which a mixture of air and fuel gas flows, to match the actual configuration of a coke oven.

[0061] The temperature calculation unit 24 inputs the heat storage chamber brick temperature, the temperatures of the fuel gas and air introduced into the heat storage chamber, the fuel gas components, the fuel gas flow rate, the air volume, the exhaust gas temperature, the exhaust gas components, and the exhaust gas flow rate into a two-dimensional model, and calculates the heat storage chamber brick temperature, the pre-combustion mixture temperature, etc. Here, the heat storage chamber brick temperature is the value calculated a time step (Δt) earlier (calculated value before Δt), which will be described later. The exhaust gas temperature is obtained from the combustion chamber temperature during the heat transfer calculation. The exhaust gas components and exhaust gas flow rate correspond to the combustion gas components and combustion gas flow rate calculated when calculating the combustion gas temperature. By using a two-dimensional model, the heat storage situation in the heat storage chamber can be accurately simulated. This improves the calculation accuracy of the gas temperature supplied to the combustion chamber 2 and allows for the representation of a time delay in which the temperature drops after the amount of heat stored in the heat storage chamber decreases when the amount of heat supplied decreases. The two-dimensional model of the heat storage chamber (heat storage chamber heat transfer calculation formula) can be expressed by the following formula.

[0062]

[0063] Here, the subscripts i and j indicate variables in the i-th row, j-th column computation area. The superscript t indicates a variable at time t. ρr is the density [kg / m³]. 3]. Cpr is the specific heat at constant pressure [kJ / (kg·K)]. Vr is the volume [m 3 ] Tr is the temperature [K]. Δt is the time step [s]. Ar is the length [m] of the boundary [m] that touches the calculation domain at row i, column j. qr is the amount of heat transported per unit length and unit time moved to row i, column j [kJ / (m·s)]. Qr is the amount of heat loss per unit time [kJ / s].

[0064] Furthermore, the formula for calculating the amount of heat transport can be expressed as follows, using the calculation area between row i-1 and column j and row i and column j as an example, in the case of heat transport between a solid and a fluid. Here, hr is the heat transfer coefficient [kJ / (K・m・s)].

[0065]

[0066] Furthermore, the formula for calculating the amount of heat transport can be expressed as follows, using the calculation area between row i-1 and column j and the calculation area between row i and column j as an example, in the case of heat transport between solids or fluids. Here, λr is the thermal conductivity [kJ / (K・m・s)].

[0067]

[0068] The heat transfer calculation unit 23 inputs the values ​​calculated by the temperature calculation unit 24 and the values ​​obtained from the input device 21 into the temperature prediction model to calculate the temperature, coal moisture content, and volatile component content at each location in the coke oven (calculation points such as bricks, coal, and combustion chamber 2). Here, the values ​​calculated by the temperature calculation unit 24 include combustion gas temperature, combustion gas flow rate, combustion gas components, specific heat, density, thermal conductivity of the combustion gas, and heat transfer coefficient in combustion chamber 2. The values ​​obtained from the input device 21 include coal charge amount, coal moisture content, volatile component content, particle size, coal density, specific heat, thermal conductivity, operation schedule, and physical properties of the bricks constituting the coke oven.

[0069] The temperature prediction model represents the horizontal direction of the coke oven as a one-dimensional point. That is, the heat transfer in all the combustion chambers 2 and carbonization chambers 3 connected in series is constructed as a one-dimensional heat transfer model. FIG. 5 shows a one-dimensional heat transfer model for the combustion chamber 2 and the carbonization chamber 3. By solving the heat transfer calculation for each calculation point, the temperature at each position of the coke oven is calculated. Here, the number of points (number of calculation points) representing the combustion chamber 2, the carbonization chamber 3, and the bricks may be any integer of 1 or more. Further, the temperature of the coal may be calculated using a specific heat obtained by weighted averaging the specific heat of the contained moisture and volatile components and the specific heat of the coal based on the volume density of each component. Here, when the boiling point of the moisture is reached, the heat supply amount to the coal may use the latent heat of vaporization. The one-dimensional model (furnace body heat transfer calculation formula) corresponding to the horizontal direction of the coke oven can be expressed by the following formula.

[0070]

[0071] Here, the subscript k indicates that it is a variable of the calculation point k. ρc is the density [kg / m 3 . Cpc is the specific heat at constant pressure [kJ / (kg·K)]. Vc is the volume [m 3 . Tc is the temperature [K]. Δt is the time step width [s]. qc is the heat transport amount per unit length per unit time moving to the calculation point k [kJ / (m ２ ·s)]. Qc is the heat loss amount per unit time [kJ / s]. The distance in the coke oven corresponding to the calculation point k−1 and the calculation point k can be adjusted according to the calculation load although the calculation accuracy increases if it is shortened, and it is not limited to a specific value. As an example, the distance in the coke oven corresponding to the calculation point k−1 and the calculation point k may be 0.1 m.

[0072] Further, the calculation formula for obtaining the heat transport amount can be expressed by the following formula, taking as an example the heat transport amount moving from the calculation point k−1 to the calculation point k in the case of heat transport between a solid and a fluid. Here, hc is the heat transfer coefficient [kJ / (K·m·s)].

[0073]

[0074] Furthermore, the formula for calculating the amount of heat transport can be expressed as follows, using the calculation area between row i-1 and column j and the calculation area between row i and column j as an example, in the case of heat transport between solids or fluids. Here, λc is the thermal conductivity [kJ / (K・m・s)].

[0075]

[0076] Furthermore, to improve calculation accuracy, the temperature prediction model is configured to perform a one-dimensional heat transfer calculation in the vertical direction according to the location of the measurement point (in this embodiment, for each of the multiple combustion chambers 2). The vertical heat transfer calculation is performed after the horizontal heat transfer calculation, and assuming that the combustion chamber temperature after the horizontal heat transfer calculation is constant, it is used for temperature prediction or parameter optimization (data assimilation) at the temperature measurement location. A feature of this invention is that it uses a combination of a one-dimensional heat transfer model in the horizontal direction and a one-dimensional heat transfer model in the vertical direction, and the accuracy of prediction at the actual temperature measurement point is improved by the linkage of the two models. Figure 6 shows a one-dimensional heat transfer model for the temperature measurement location (in this embodiment, the location of the thermocouples in the multiple combustion chambers 2). The temperature at each temperature measurement location in all combustion chambers 2 (or all carbonization chambers 3) is constructed as a one-dimensional heat transfer model from each of the combustion chambers 2 (or all carbonization chambers 3). Heat transfer calculations for each calculation point are performed in the same way as for the one-dimensional heat transfer model in the horizontal direction. A one-dimensional model corresponding to the vertical direction of a coke oven (the formula for calculating heat transfer to the top) can be expressed by the following formula:

[0077]

[0078] Here, the subscript l indicates that it is a variable of the calculation point l. ρb is the density [kg / m³]. 3 ]. Cpb is the specific heat at constant pressure [kJ / (kg·K)]. Vb is the volume [m 3 ]. Tb is the temperature [K]. Δt is the time step [s]. qb is the heat transfer rate per unit length per unit time moved to the calculation point l [kJ / (m ２Qb is the heat loss per unit time [kJ / s]. The distance between calculation point l-1 and the coke oven corresponding to calculation point l can be shortened to improve calculation accuracy, but it can be adjusted according to the calculation load and is not limited to a specific value. For example, the distance between calculation point l-1 and the coke oven corresponding to calculation point l may be 0.1 m.

[0079] Furthermore, the formula for calculating the amount of heat transport can be expressed as follows, using the example of heat transport between solids moving from calculation point k-1 to calculation point k: Here, λb is the thermal conductivity [kJ / (K・m・s)].

[0080]

[0081] (Calculation method for bricks in contact with combustion chamber 2) In this embodiment, in order to perform heat transfer calculations in the vertical direction of the combustion chamber, it is necessary to calculate the heat transfer between the gas in the combustion chamber 2 and the bricks on the upper surface (top of the furnace) of the combustion chamber 2. The amount of heat loss of the gas in the combustion chamber 2 depends on the heat transfer with the adiabatic bricks in the horizontal direction. Assuming that the heat loss from the top of the furnace due to the adiabatic bricks is small and negligible, the combustion chamber gas temperature is calculated using the temperature after the horizontal heat transfer calculation as a constant value. Under the above assumption, the amount of solid-fluid heat transfer between the gas in the combustion chamber 2 and the brick surface at the top of the combustion chamber 2 can be expressed by the following formula. Here, the subscript 1 indicates a parameter of the gas in the combustion chamber 2. Also, the subscript 2 indicates a parameter of the brick surface at the top of the combustion chamber.

[0082]

[0083] Figure 10 compares the temperature prediction accuracy when heat transfer calculations are performed in the vertical direction using the one-dimensional heat transfer model shown in Figure 6, with and without such calculations. Specifically, when heat transfer calculations are performed in the vertical direction, the actual measurement temperature (temperature of the bricks at the top of the combustion chamber) is predicted, while when they are not performed, only the gas temperature inside combustion chamber 2 (gas temperature inside the combustion chamber) is predicted.

[0084] In the graph in Figure 10, the vertical axis shows the prediction error of the furnace group average temperature, dimensionless by the standard deviation σ of the prediction error when only the gas temperature in combustion chamber 2 is used. The horizontal axis represents the elapsed time (h) from the start of the calculation.

[0085] As a result, when heat transfer calculations were performed in the vertical direction, the prediction error was within ±1σ, indicating high prediction accuracy. On the other hand, when heat transfer calculations were not performed in the vertical direction and only the gas temperature in combustion chamber 2 was used, the prediction error expanded to more than 2σ, indicating a decrease in accuracy.

[0086] From the above, it was confirmed that incorporating vertical heat transfer calculations significantly improves the accuracy of temperature prediction for coke ovens.

[0087] (Optimization of the temperature prediction model) The temperature prediction model may be optimized so that the temperature, coal moisture content, and volatile component content at each position of the coke oven calculated by the heat transfer calculation unit 23 are reflected. The optimization is performed to prevent the calculated values ​​from deviating from the actual values ​​due to error factors such as the aging of the furnace body or coke adhering to the walls of the carbonization chamber 3. The optimization is performed by adjusting the heat transport amount or heat loss amount at one or more of the above calculation points.

[0088] When optimization is performed, the temperature prediction model is configured so that parameters for heat transfer can be adjusted. The following equation is used for the one-dimensional model corresponding to the horizontal direction of the coke oven (furnace heat transfer calculation formula).

[0089]

[0090] Furthermore, the following formula is used to calculate the amount of heat transported.

[0091]

[0092] Here, R and S are variables (parameters) that can be adjusted by optimization. By adjusting R or S, the amount of heat transported horizontally in the coke oven is optimized so that the error between the calculated value and the actual value at the temperature measurement position is reduced. Since increasing the number of parameters to be adjusted complicates the calculation, it is preferable to use as few correction terms as possible while improving accuracy. Here, it is preferable to use R, which adjusts the amount of heat transported between the combustion chamber 2 and the bricks and between the bricks and the carbonization chamber 3 in equation [Equation 13], or S, which adjusts the amount of heat loss Qc in equation [Equation 12], as the parameters to be adjusted.

[0093] Figure 8 is a flowchart illustrating the furnace temperature prediction method, including the optimization process. The same reference numerals are used for processes identical to those in Figure 7, and their explanations are omitted. The above parameters are adjusted based on the error, which is the difference between the calculated and actual temperatures of the combustion chamber 2 (or carbonization chamber 3) from a predetermined time point in the past (t = -T) to the prediction time (t = 0).

[0094] In the optimization flag step (S31), it is determined whether to perform an optimization calculation (i.e., optimizing adjustable parameters) based on the number of times the optimization calculation (i.e., optimizing adjustable parameters) has been performed or the time elapsed since the start of the calculation. If the optimization calculation has been performed up to the maximum number of times, or if the maximum time has elapsed since the start of the calculation (No in S31), the process proceeds to the second calculation flow (Pb). If the optimization calculation has not been performed up to the maximum number of times, and the maximum time has not elapsed since the start of the calculation (Yes in S31), the process proceeds to the third calculation flow (Pc, optimal parameter calculation).

[0095] The furnace temperature prediction unit 25 calculates the error between the calculated value and the actual value at the temperature measurement location. The error may be calculated from a predetermined time (T) past point in time (t = -T) to the predicted point in time (t = 0). For example, the time average of the calculated value and the actual value of the temperature from t = -T to t = 0 may be calculated, and the difference between the calculated time averages may be taken as the error. Alternatively, a weighted difference in time averages may be used, or RMSE (Root Mean Squared Error) may be used. If the error satisfies the judgment condition (Yes in S32), the process proceeds to the second calculation flow (Pb). For example, the judgment condition may be that the error is less than a predetermined threshold. If the error does not satisfy the judgment condition (No in S32), the process proceeds to S33.

[0096] The furnace temperature prediction unit 25 adjusts the parameters (for example, the coefficient R of the heat transport amount in equation [Equation 13] and the coefficient S of the heat loss amount Qc in equation [Equation 12]) to minimize the error (S33). The furnace temperature prediction unit 25 may repeatedly perform calculations while gradually changing the parameters, or it may perform adjustments based on the correlation between the adjusted parameters and the calculated temperature. Using the adjusted parameters, the time is returned to a predetermined time (T) in the past (t = -T) (S34), and the first calculation flow (Pa) is executed again.

[0097] The parameters are adjusted independently for combustion chamber 2 and carbonization chamber 3. The parameters for combustion chamber 2 are adjusted at each temperature measurement timing (hourly in the case of automatic measurement), while the parameters for carbonization chamber 3 are adjusted when the fire in each carbonization chamber 3 extinguishes. This allows for optimization in accordance with the changes in the state of each chamber.

[0098] As a parameter adjustment method, for combustion chamber 2, the coefficient S of the heat loss term from the brick surface facing combustion chamber 2 is adjusted so that the error between the temperature of the measurement section (e.g., the brick surface at the top of the combustion chamber) obtained by the vertical heat transfer calculation and the actually measured temperature is minimized. This adjustment is performed by repeatedly changing the value of coefficient S little by little and searching for the value that minimizes the difference between the predicted temperature and the measured temperature. The adjustment period is performed at each temperature measurement timing (every hour in the case of automatic measurement, for example, every hour).

[0099] On the other hand, for carbonization chamber 3, at the time of fire extinction (the timing when the carbonization of coal is completed), the coefficient R of the heat transport term to the bricks in contact with the coal seam is adjusted so that the temperature at the center of the coal seam in carbonization chamber 3 matches the carbonization temperature of approximately 900 to 1000°C. This adjustment is performed by repeatedly changing the value of the coefficient R in steps, so as to minimize the error between the temperature at the center of the coal seam at fire extinction and the carbonization temperature. The adjustment cycle is performed each time fire extinction occurs in carbonization chamber 3.

[0100] Here, if the value of coefficient R fluctuates significantly, it will affect the thermal conductivity from the combustion chamber gas to the coal seam through the bricks between the combustion chamber 2 and the carbonization chamber 3. Therefore, it is desirable to set upper and lower limits for coefficient R. In addition, coefficient S may be adjusted to compensate for fluctuations in coefficient R.

[0101] Figure 11 shows a comparison of the prediction accuracy of the furnace group average temperature, which is the average temperature of all combustion chambers 2 and carbonization chambers 3 constituting the furnace group, when the coefficient R, which is a parameter for each combustion chamber 2, is adjusted with a 1-hour period (learning enabled) and when no adjustment is performed (no learning enabled).

[0102] The vertical axis in the figure represents the dimensionless prediction error based on the standard deviation of the prediction error for the average furnace group temperature without learning. The horizontal axis represents the elapsed time, with the start of the calculation set to 0h. With learning, the prediction error remains stable around 0, whereas without learning, the prediction error tends to increase over time. In other words, it was confirmed that high temperature prediction accuracy can be maintained even over long periods of time by periodically adjusting the coefficient R, which is an adjustment parameter for each combustion chamber 2.

[0103] (Furnace Temperature Control) The control terminal 10 compares the furnace temperature predicted by the furnace temperature prediction device 20 with the target temperature and calculates the amount of heat to supply so that the furnace temperature becomes the target furnace temperature. Based on the calculated amount of heat to supply, the control terminal 10 adjusts the control valve 5 and the kiln-specific control valve 6.

[0104] As an example of conventional technology, PID control is known, which calculates the amount of heat supplied so that the furnace temperature reaches the target temperature, using three elements: the deviation between the furnace temperature at the predicted time and the target temperature, the integral value of the deviation, and the derivative value. In the method disclosed herein, the calculation is performed using the deviation between the future furnace temperature and the target temperature, rather than the furnace temperature at the predicted time. Therefore, it is possible to calculate the amount of heat supplied that takes future temperature fluctuations into account, enabling more accurate furnace temperature control.

[0105] Figure 9 is a flowchart showing the processing of the furnace temperature prediction method (furnace temperature control method), including the furnace temperature control process. The same reference numerals are used for the same processes as in Figure 7, and their explanations are omitted.

[0106] If the control terminal 10 changes the heat supply amount based on the deviation between the future furnace temperature and the target temperature (Yes in S41), it transmits information about the changed heat supply amount to the furnace temperature prediction device 20. The changed heat supply amount may be set to, for example, n times the value before the change (latest value) (n is a positive real number). The furnace temperature prediction device 20 executes a second calculation flow (Pb) reflecting the information about the changed heat supply amount as the time to be predicted (t=0) (S42). If the control terminal 10 does not change the heat supply amount (No in S41), the series of processes ends. The calculated future furnace temperature may be displayed on the display device 30.

[0107] (Fire-down time control) The target temperature of the combustion chamber 2 adjacent to each carbonization chamber 3 may be individually adjusted so that the fire-down time of each carbonization chamber 3 is aligned with a common target time (to match the common target fire-down time). This may be individually set or adjusted in the future prediction calculation in the second calculation flow (Pb) shown in Figure 7. That is, if it is expected that multiple carbonization chambers 3 will fire-down within the prediction period, the target furnace temperature (or the amount of adjustment thereto) for each future combustion chamber 2 may be individually set or adjusted so that the predicted fire-down time of each carbonization chamber 3 is aligned with the target time. As a specific example, the time at which the coal seam center temperature of each carbonization chamber 3 reaches the carbonization temperature may be predicted by one-dimensional heat transfer calculation to determine whether fire-down is possible, and a recommended temperature adjustment amount ΔT (recommended) for each carbonization chamber 3 based on the difference from the target may be calculated and allocated to the target furnace temperatures of the adjacent left and right combustion chambers 2.

[0108] Here, in calculating the recommended temperature adjustment amount ΔT (recommended), the difference between the predicted next ignition time (NCT (predicted)) and the target ignition time (NCT_ref) for each carbonization chamber 3 is determined. This difference is converted using the temperature sensitivity coefficient coef (= ∂NCT / ∂T) with respect to ignition time, and the recommended temperature adjustment amount (unit: °C) obtained by multiplying it by a relaxation coefficient A (0 < A ≤ 1) that suppresses excessive adjustment is the recommended temperature adjustment amount ΔT (recommended). For example, the recommended temperature adjustment amount is defined as ΔT (recommended) = A × {NCT_ref - NCT (predicted)} / coef. When ΔT (recommended) is positive, it indicates an operation in the direction of increasing the temperature (shortening the ignition time). When ΔT (recommended) is negative, it indicates an operation in the direction of decreasing the temperature (extending the ignition time). Here, the sensitivity coefficient coef may be determined by linearizing it around the one-dimensional heat transfer model of the present invention and evaluating ∂NCT / ∂T.

[0109] The calculation formulas for these ΔT (recommended) and the distribution procedure from the carbonization chamber 3 to the combustion chamber 2 are based on the description in, for example, Japanese Patent Publication No. 6673490, and the details are omitted here by referencing that description.

[0110] (Embodiment 2) As a configuration different from the above embodiment (Embodiment 1), there is an embodiment (Embodiment 2) in which the present invention is applied to a coke oven employing an interval combustion method. The interval combustion method is a method of intermittently controlling the amount of heat supplied by stopping the fuel supply for a certain period of time during the combustion cycle, as shown in Figure 12. This method is effective in improving combustion efficiency or distributing the heat load, but it has the problem that the temperature behavior becomes complicated because heat is removed from the furnace body by the air flowing into the combustion chamber 2 during the stop period.

[0111] In particular, with the interval combustion method, the stop period can be adjusted in seconds, and in order to accurately reproduce the temperature fluctuations during the stop period, the time step size in the physical model must be set to 1 second or less. This is because the temperature change due to the presence or absence of combustion gas supply occurs in a very short time, and if the time step size is large, it will not be able to capture the change, and the prediction accuracy will decrease significantly.

[0112] However, reducing the time step size increases the number of transient calculations, significantly increasing the computational load. This makes it difficult to apply the method to real-time prediction or control in actual equipment. Therefore, as a common countermeasure, as shown in Figure 10, a method is used to reduce the computational load by averaging input items such as fuel flow rate over a certain period and increasing the time step size.

[0113] However, this averaging method has limitations. In actual operation, air flows through combustion chamber 2 during shutdown periods, and heat is continuously removed from the brick surface. In contrast, the averaged physical model treats the situation as if fuel supply were always continuous, so the heat removal by air is not reflected, resulting in a discrepancy in temperature behavior.

[0114] In particular, if the shutdown period is extended, the actual furnace temperature will decrease quickly due to the cessation of combustion gas supply and increased heat removal by air. However, in the physical model, heat removal is not taken into account even when the fuel supply is stopped, so the temperature drop only occurs after events such as extrusion and charging. For this reason, while the temperature reaches a steady state in the actual phenomenon, it may take more than 10 hours to reach a steady state in the physical model.

[0115] To resolve this discrepancy, a correction process is necessary to reflect the heat removal by air during the shutdown period in the physical model. As part of this correction process, when calculating the combustion gas temperature in combustion chamber 2, the amount of heat removed is calculated using the air flow rate, exhaust gas temperature, ambient temperature, and specific heat of air, and this is subtracted from the calorific value of the combustion gas, thereby improving the accuracy of temperature prediction.

[0116] In this embodiment, in order to improve the temperature prediction accuracy for coke ovens employing the interval combustion method, the temperature calculation unit 24 uses the shutdown period as an input value in addition to the fuel gas components, fuel gas flow rate, air amount, and pre-combustion mixture temperature. This makes it possible to consider the effect of heat removal due to air inflow that occurs while the fuel supply is stopped when calculating the combustion gas temperature.

[0117] <First Correction Method> In the first correction method, when calculating the combustion gas temperature of the combustion chamber 2, the amount of heat generated per unit time during complete combustion of the fuel gas E is used as the amount of heat removed by air (Eｌｏｓｓ The temperature is corrected by the amount of heat removed by air (E). In the interval combustion method, if the stop period is extended, the effect of heat removal by air becomes significant, so the temperature calculation unit 24 performs the following correction process. First, the amount of heat removed by air (E ｌｏｓｓ The following formula is used to calculate it.

[0118]

[0119] Here, E ｌｏｓｓ This is the amount of heat removed by air [kJ / s]. Q ａｉｒ The airflow rate during the shutdown period is [Nm³] 3 / s] is. T ｄｒｙ is the exhaust gas temperature [°C]. ｏｕｔ ρ is the outside temperature [°C]. ａｉｒ The specific heat of air [(kJ / Nm)] 3 ) / K].

[0120] The temperature calculation unit 24 then uses the combustion gas components, flow rate, calorific value, and pre-combustion mixture temperature to calculate the combustion gas temperature T, which satisfies the following equation such that the amount of heat consumed to raise the temperature of the combustion gas from the pre-combustion mixture temperature Tb is equal to the calorific value of the fuel gas.

[0121]

[0122] Here, E is the calorific value of the fuel gas per unit time during complete combustion [kJ / s]. Tb is the pre-combustion mixture temperature [°C]. ρ is the specific heat of each gas type. Q' is the flow rate of each gas type after combustion. Δt is the time step. GT is the fuel supply time [s] within the time step Δt. PT is the stop period [s] within the time step Δt.

[0123] This amount of heat removed (E ｌｏｓｓ By subtracting this from the calorific value E of the fuel gas during complete combustion per unit time, a combustion gas temperature close to the actual temperature behavior can be calculated. If the air flow rate is not measured, the total flow rate is estimated from the air-to-air ratio and fuel flow rate at the time of fuel supply and used as the air flow rate during the shutdown period.

[0124] <Second Correction Method> The effect of heat removal by air can also be expressed by correcting the air ratio. In the second correction method, the rate of change of the shutdown period (PTｒａｔｅ The air ratio is corrected according to the following formula.

[0125]

[0126] Here, AR ａｉｒ This is the corrected air-fuel ratio [-]. AR ｇａｓ This represents the air-fuel ratio [-] during fuel supply. PT ｒａｔｅ is the rate of variation in the shutdown period (the ratio of the shutdown period to the supply period of fuel gas to combustion chamber 2). α is an arbitrarily set constant parameter (air ratio correction coefficient α). By optimizing α based on past operating results, it is possible to maintain the accuracy of temperature prediction in coke ovens employing the interval combustion method.

[0127] These correction processes (first correction method, second correction method) are applied to each combustion chamber 2 from the time of the extended shutdown period until the adjacent kiln is extruded and charged.

[0128] According to this embodiment, even in a coke oven employing an interval combustion method, it is possible to predict the future temperature of the combustion chamber 2 or carbonization chamber 3 with high accuracy by performing a correction process that takes into account heat removal by air during the shutdown period. This prevents the decrease in temperature prediction accuracy when the shutdown period is extended, which was difficult with conventional physical models, and enables more stable furnace temperature control.

[0129] Figure 13 compares the furnace temperature prediction accuracy of Embodiment 1 and Embodiment 2 for a coke oven employing the interval combustion method. Figure 13 also shows the trend of the furnace group average temperature prediction error and the trend of the supplied heat amount at the same time. The first vertical axis (left) shows the average prediction error of the furnace group average temperature, dimensionless using the standard deviation of the average prediction error of the furnace group average temperature when using Embodiment 1. The second vertical axis (right) shows the supplied heat amount, dimensionless using the average value of the supplied heat amount at the time shown in the graph. During the period around 60h when the supplied heat amount decreased by approximately 20%, the furnace temperature prediction error of Embodiment 1 increased over time, while the furnace temperature prediction error of Embodiment 2 remained small, demonstrating favorable results.

[0130] As described above, the furnace temperature prediction method, furnace temperature control method, furnace temperature prediction device 20, and furnace temperature control device according to this embodiment enable highly accurate furnace temperature prediction using a physical model suitable for control in an actual machine, through the above configuration.

[0131] While embodiments of this disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art will find it easy to make various modifications or alterations based on this disclosure. Therefore, it should be noted that these modifications or alterations are included within the scope of this disclosure. For example, the functions included in each component or step can be rearranged in a logically consistent manner, and multiple components or steps can be combined into one or divided. Embodiments relating to this disclosure can also be realized as programs executed by a processor in the device or as storage media recording such programs. These should also be understood to be included within the scope of this disclosure.

[0132] 2 Combustion chamber 3 Carbonization chamber 4 Gas main 5 Control valve 6 Individual kiln control valve 10 Control terminal 20 Furnace temperature prediction device 21 Input device 23 Heat transfer calculation unit 24 Temperature calculation unit 25 Furnace temperature prediction unit 26 Output device 30 Display device

Claims

1. A furnace temperature prediction method performed by a furnace temperature prediction device that predicts the temperature of a combustion chamber or a carbonization chamber in a coke oven, in which a combustion chamber having a heat storage chamber at the bottom and a carbonization chamber are alternately connected to form a furnace group, wherein the temperature prediction model is a physical model capable of transient calculations that performs temperature prediction calculations for the entire furnace group, including the combustion chamber, the carbonization chamber and the heat storage chamber, and includes: acquiring operational performance information including information on coal charging to the carbonization chamber, information related to the coal being charged, information on fuel supplied to the combustion chamber, the target temperature of the combustion chamber or the carbonization chamber, and the temperature of the combustion chamber or the carbonization chamber at the time of prediction; and predicting the temperature of each of the multiple combustion chambers or multiple carbonization chambers after a predetermined time based on the operational performance information, using the respective temperature prediction models for each of the multiple combustion chambers or multiple carbonization chambers.

2. The furnace temperature prediction method according to claim 1, wherein the temperature prediction model is a physical model constructed by constructing the heat transfer in all the connected combustion chambers and carbonization chambers as a horizontal one-dimensional heat transfer model, the heat transfer in all the heat storage chambers as a two-dimensional heat transfer model, and the temperature at each temperature measurement position in all the combustion chambers or all the carbonization chambers as a vertical one-dimensional heat transfer model from each of the combustion chambers or all the carbonization chambers.

3. The furnace temperature prediction method according to claim 2, characterized in that the vertical one-dimensional heat transfer model calculates the vertical heat transfer including bricks from the center of each combustion chamber or carbonization chamber to the temperature measurement position at the top of the furnace, and calculates the temperature at the temperature measurement position based on the calculation results of the horizontal one-dimensional heat transfer model.

4. The furnace temperature prediction method according to any one of claims 1 to 3, characterized in that the temperature prediction by the temperature prediction model includes a first calculation flow that reproduces the equipment state based on past operational performance information up to the prediction time, and a second calculation flow that predicts the temperature based on future operational information from the prediction time to a predetermined time ahead.

5. The furnace temperature prediction method according to any one of claims 1 to 4, characterized in that the combustion gas temperature of the combustion chamber in the temperature prediction model is calculated such that the amount of heat generated during complete combustion, calculated based on the fuel gas components and fuel gas flow rate, is equal to the amount of heat required for the temperature rise, calculated based on the pre-combustion mixture temperature, the specific heat of each combustion gas component, and the post-combustion fuel gas flow rate.

6. The furnace temperature prediction method according to claim 5, characterized in that, when the coke oven employs an interval combustion method, the amount of heat removed by air inflow during the period when the fuel gas is stopped from entering the combustion chamber is calculated and a correction is made to reflect this in the combustion gas temperature.

7. The furnace temperature prediction method according to claim 5, characterized in that, when the coke oven employs an interval combustion method, the combustion gas temperature is calculated by correcting the air ratio of the fuel gas based on the ratio of the fuel gas stop period to the fuel gas supply period to the combustion chamber.

8. The furnace temperature prediction method according to any one of claims 1 to 7, wherein the temperature prediction model is configured to allow adjustment of parameters for heat transfer, and further comprises adjusting the parameters based on an error which is the difference between a calculated value and an actual value of the temperature of the combustion chamber or the carbonization chamber from a point in time a predetermined time prior to the prediction point up to the prediction point.

9. The furnace temperature prediction method according to claim 8, characterized in that the parameters of the temperature prediction model are optimized based on the error between the calculated value at the temperature measurement position, which is calculated by a one-dimensional heat transfer model in the vertical direction, and the actual temperature measurement value.

10. The furnace temperature prediction method according to claim 8 or 9, characterized in that the optimization of the parameters is performed for the combustion chamber parameters at each temperature measurement timing, and for the carbonization chamber parameters at the time of fire extinguishing in each carbonization chamber.

11. The furnace temperature prediction method according to any one of claims 1 to 10, wherein the predetermined time is set to a time greater than the time constant of the coke oven.

12. A furnace temperature control method that predicts the temperature of the combustion chamber or the carbonization chamber using the furnace temperature prediction method described in any one of claims 1 to 11, and controls the fuel supply by calculating the fuel supply amount as a manipulated amount so that the predicted temperature approaches the target temperature.

13. A furnace temperature control method using the furnace temperature prediction method described in claim 4, wherein, in the second calculation flow, when a fire extinction in multiple carbonization chambers is expected within the prediction target period, the future target furnace temperature or adjustment amount for each combustion chamber adjacent to each carbonization chamber is individually determined so that the predicted fire extinction time for each carbonization chamber is aligned with a common target fire extinction time, and the fuel supply amount is calculated as an operating amount based on the target furnace temperature or adjustment amount and the fuel supply is controlled, as described in claim 12.

14. A furnace temperature prediction device for a coke oven in which a combustion chamber having a heat storage chamber at the bottom and a carbonization chamber are alternately connected to form a furnace group, the device predicts the temperature of the combustion chamber or the carbonization chamber using a temperature prediction model, wherein the temperature prediction model is a physical model capable of transient calculations that performs temperature prediction calculations for the entire furnace group including the combustion chamber, the carbonization chamber and the heat storage chamber, and includes an input device that acquires operational performance information including information on coal charging to the carbonization chamber, information related to the coal being charged, information on fuel supplied to the combustion chamber, a target temperature for the combustion chamber or the carbonization chamber, and the temperature of the combustion chamber or the carbonization chamber at the time of prediction, and a furnace temperature prediction unit that predicts the temperature of each of the multiple combustion chambers or multiple carbonization chambers after a predetermined time based on the operational performance information, using the respective temperature prediction models for each of the multiple combustion chambers or multiple carbonization chambers.

15. A furnace temperature control device that predicts the temperature of the combustion chamber or the carbonization chamber using the furnace temperature prediction device described in claim 14, and controls the supply of fuel by calculating the amount of fuel to be supplied as an operand so that the predicted temperature approaches the target temperature.

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