Physical distribution plan generation method and physical distribution plan generation device

Abstract

To provide a physical distribution plan generation method and a physical distribution plan generation device capable of generating a physical distribution plan which contributes to energy saving after guaranteeing the establishment of an operation.SOLUTION: This physical distribution plan generation method comprises: acquiring input information; updating the mounting temperature of a slab to a heating furnace on the basis of the surface temperature of the slab when it is extracted from a continuous casting machine, initial inventory information at a slab yard, and a facility item value between a steel making process and a rolling process by the continuous casting machine; generating physical distribution plan candidates at the slab yard on the basis of the mounting plan to the heating furnace; executing simulation for the physical distribution plan candidates; and selecting a physical distribution plan with heating constraints taken into account.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to a distribution plan creating method and a distribution plan creating device, and in particular to a distribution plan creating method and a distribution plan creating device that create a distribution plan taking into account the state of a slab when it is charged into a heating furnace before a rolling process in which hot rolling is performed. [Background technology]

[0002] Many of the slabs cast in the steelmaking process are stored in yards (hereinafter referred to as slab yards) where the slabs are kept dissipating heat until they are rolled. After being incorporated into a logistics plan, the slabs are reheated in a heating furnace up to a temperature suitable for rolling and then rolled. While the slabs are stored in the slab yard, they lose heat through radiation and heat transfer, causing their temperature to drop. Reheating the slabs at the highest possible temperature is useful not only for improving production efficiency, but also for saving energy by suppressing heat dissipation.

[0003] Patent Document 1 describes a method for calculating the temperature transition of a slab from the torch cut time of the slab, the slab temperature during continuous casting, the transportation plan, and pile information in the slab yard, and predicting the temperature of the slab when it is charged into a heating furnace.

[0004] The technology of Patent Document 2 expresses the difference between the current temperature and the target temperature of each slab as a cost function when the slabs to be cooled and heated are stored in a slab yard in which the slabs are overlapped with each other. The overlapping state of each slab is determined so as to minimize the cost function, and the slabs are rearranged according to the overlapping state determined. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 6380510 [Patent Document 2] Patent No. 6354952 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the technology of Patent Document 1 predicts the slab charging temperature, but does not provide a means to further improve the charging temperature. Also, the technology of Patent Document 2 can implement a transportation plan guidance with the goal of improving the temperature of all slabs in the yard, but the cost function does not take into account the transportation load of the slab yard.

[0007] In view of the above circumstances, an object of the present disclosure is to provide a logistics plan creation method and a logistics plan creation device that can create a logistics plan that contributes to energy conservation while ensuring the feasibility of operations. [Means for solving the problem]

[0008] (1) A method for creating a logistics plan according to an embodiment of the present disclosure includes: A method for creating a logistics plan for a slab yard that stores slabs to be extracted from a continuous casting machine and charged into a heating furnace or slabs to be extracted from the heating furnace and charged into the heating furnace again without being hot rolled, in a rolling process in which a slab is hot rolled by a hot rolling mill, comprising: obtaining input information including inventory information, casting information, heat information, rolling information, and equipment information; updating the charging temperature of the slab into the heating furnace based on the surface temperature of the slab when extracted from the continuous casting machine, which is included in the casting information, initial inventory information at the slab yard, which is included in the inventory information, and equipment specification values ​​between the steelmaking process by the continuous casting machine and the rolling process, which are included in the equipment information; Creating a candidate logistics plan for the slab yard based on the heating information and the charging plan for the heating furnace included in the rolling information; The method includes running a simulation for the candidate logistics plans and selecting a logistics plan taking into account the heating constraints.

[0009] (2) As an embodiment of the present disclosure, in (1), The heating constraints are taken into consideration by selecting a logistics plan based on an evaluation formula that includes a term related to improving the charging temperature of the slabs into the heating furnace and a term related to reducing the difference in charging temperatures of adjacent slabs in the heating furnace.

[0010] (3) As an embodiment of the present disclosure, in (1) or (2), Creating the candidate logistics plan is performed taking into account crane grab constraints and pile constraints.

[0011] (4) As an embodiment of the present disclosure, in any one of (1) to (3), The charging temperature is updated by predicting the charging temperature of the slab into the heating furnace using a slab heat dissipation model equation that assumes no heat transfer between slabs.

[0012] (5) As an embodiment of the present disclosure, in (4), The heat release model equation is a regression equation based on the residence time until the slab is charged into the heating furnace and the difference between the predicted slab temperature value and the actual slab temperature value when the slab is charged into the heating furnace.

[0013] (6) A logistics planning device according to an embodiment of the present disclosure, A logistics plan creation device that creates a logistics plan for a slab yard that stores slabs that are extracted from a continuous casting machine and charged into a heating furnace, or slabs that are extracted from the heating furnace and then charged into the heating furnace again without being hot rolled, in a rolling process in which a slab is hot rolled by a hot rolling mill, comprising: An acquisition unit that acquires input information including inventory information, casting information, heating information, rolling information, and equipment information; updating the charging temperature of the slab into the heating furnace based on the surface temperature of the slab when extracted from the continuous casting machine, which is included in the casting information, the initial inventory information at the slab yard, which is included in the inventory information, and the equipment specification values ​​between the steelmaking process by the continuous casting machine and the rolling process, which are included in the equipment information; A candidate physical distribution plan for the slab yard is created based on the heating information and the charging plan for the heating furnace included in the rolling information; The system further comprises a calculation unit that executes a simulation for the candidates for the logistics plan and selects a logistics plan taking into account the heating constraint.

[0014] (7) As an embodiment of the present disclosure, in (6), The heating constraints are taken into consideration by selecting a logistics plan based on an evaluation formula that includes a term related to improving the charging temperature of the slabs into the heating furnace and a term related to reducing the difference in charging temperatures of adjacent slabs in the heating furnace.

[0015] (8) As an embodiment of the present disclosure, in (6) or (7), Creating the candidate logistics plan is performed taking into account crane grab constraints and pile constraints.

[0016] (9) As an embodiment of the present disclosure, in any one of (6) to (8), The charging temperature is updated by predicting the charging temperature of the slab into the heating furnace using a slab heat dissipation model equation that assumes no heat transfer between slabs.

[0017] (10) As an embodiment of the present disclosure, in (9), The heat release model equation is a regression equation based on the residence time until the slab is charged into the heating furnace and the difference between the predicted slab temperature value and the actual slab temperature value when the slab is charged into the heating furnace. Effect of the Invention

[0018] According to the present disclosure, it is possible to provide a logistics plan creation method and a logistics plan creation device that can create a logistics plan that contributes to energy conservation while ensuring the feasibility of operations. [Brief description of the drawings]

[0019] [Figure 1]FIG. 1 is a diagram illustrating a manufacturing line to which a logistics planning method according to an embodiment of the present disclosure is applied. [Diagram 2] FIG. 2 is a schematic diagram of a slab yard. [Diagram 3] FIG. 3 is a diagram illustrating a configuration example of a distribution plan creation device and a process flow of a distribution plan creation method according to an embodiment of the present disclosure. [Figure 4A] FIG. 4A is a diagram for explaining heat transfer in a slab. [Figure 4B] FIG. 4B is a diagram for explaining heat transfer in the slab. [Figure 4C] FIG. 4C is a diagram for explaining heat transfer in the slab. [Diagram 5] FIG. 5 is a diagram showing a process for determining a candidate logistics plan. [Figure 6] FIG. 6 is a diagram showing a process flow for creating a candidate group for materials to be replaced. [Figure 7] FIG. 7 is a diagram showing a process for determining feasible rearrangement order candidates. [Figure 8] FIG. 8 is a diagram for explaining the slab width difference. [Figure 9] FIG. 9 is a diagram showing a process for determining feasible logistics plan candidates. [Figure 10] FIG. 10 is a diagram showing examples of candidates for reassignment. [Figure 11] FIG. 11 is a diagram showing the difference in error depending on whether or not the correction formula is applied. [Figure 12] FIG. 12 is a physical property value table of carbon concentration. [Figure 13] FIG. 13 shows the weighting coefficients according to carbon concentration. [Figure 14] FIG. 14 shows the equipment specification values. [Figure 15] FIG. 15 is a criteria table regarding pile constraints in a slab yard. [Figure 16] FIG. 16 shows information about the slabs in the slab yard. [Figure 17] FIG. 17 shows information regarding the casting plan. [Figure 18] FIG. 18 shows information regarding charging planning. [Figure 19] FIG. 19 shows information about the updated charging plan. [Figure 20] FIG. 20 is a diagram showing examples of candidates for reassignment. [Figure 21] FIG. 21 is a diagram showing a logistics plan candidate for pattern P1. [Figure 22] FIG. 22 is a diagram showing a logistics plan candidate for pattern P2. [Figure 23] FIG. 23 is a diagram showing a logistics plan candidate for pattern P3. [Figure 24] FIG. 24 is a physical property table. [Diagram 25] FIG. 25 shows information about the charging plan based on pattern P1. [Figure 26] FIG. 26 shows information about the charging plan based on pattern P3. [Figure 27] FIG. 27 is a diagram in which the actual temperature has been added to FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, a distribution plan creation method and a distribution plan creation device 10 (see FIG. 3) according to an embodiment of the present disclosure will be described with reference to the drawings.

[0021] FIG. 1 is a diagram illustrating a manufacturing line to which the logistics planning method according to this embodiment is applied. Slabs obtained by casting molten steel in a continuous casting machine are transported to the line of a hot rolling mill by a traverse cart. Each slab is heated in a heating furnace before being rolled in a hot rolling mill. Here, slabs that arrive at the hot rolling mill are divided into direct delivery materials that are directly charged into the heating furnace via a conveyor table after arrival, and non-direct delivery materials that are temporarily stored in a slab yard and then charged. In the slab yard, the arriving slabs can be temporarily stored.

[0022] FIG. 2 is a schematic diagram of a slab yard. When non-direct shipment materials are transported to the slab yard and temporarily stored, the slabs on the traverse carts are lifted by a crane and piled up inside the slab yard (hereinafter sometimes referred to as "inside the yard"). The slabs in the yard are incorporated into the rolling plan, and when it is their turn to be loaded into the heating furnace next, they are unloaded from the yard onto the traverse cart by a crane and loaded into the heating furnace. When the target slab is not on the top of the pile at the time of unloading, a relocation operation is performed to move the slabs stacked above the target slab to another pile, and then the unloading operation is performed. The relocation makes it possible to load the slabs into the heating furnace in an order different from the order of arrival from the continuous casting machine. The slabs loaded into the heating furnace are heated to an appropriate temperature and then extracted from the heating furnace. The slabs are extracted from the heating furnace on a first-in, first-out basis and hot-rolled.

[0023] FIG. 3 is a diagram showing an example of the configuration of the distribution plan creation device 10 according to this embodiment, and the arrows indicate the process flow of the distribution plan creation method executed by the distribution plan creation device 10. As described above, the distribution plan creation device 10 creates a distribution plan for a slab yard in a rolling process in which a hot rolling mill hot-rolls a slab. The slab yard stores, as non-direct shipment materials, slabs that are extracted from a continuous casting machine and charged into a heating furnace, or slabs that are extracted from a heating furnace and charged into the heating furnace again without being hot-rolled. In this embodiment, in a manufacturing line to which the distribution plan creation method is applied, a steelmaking process using a continuous casting machine and a rolling process in which a slab cast by a continuous casting machine is hot-rolled by a hot rolling mill are operated in a synchronized manner.

[0024] The logistics plan creation device 10 includes an acquisition unit 11, a calculation unit 12, and an output unit 13. The logistics plan creation device 10 may have a hardware configuration, for example, a computer. The logistics plan creation device 10 may also have a software configuration as described below. One or more programs used to control the operation of the logistics plan creation device 10 are stored in a storage device accessible from the logistics plan creation device 10. When the program stored in the storage device is read by a processor (for example, a CPU) included in the logistics plan creation device 10, it causes the processor to function as the acquisition unit 11, the calculation unit 12, and the output unit 13.

[0025] The distribution plan creation device 10 is configured to be able to communicate with a host system 20. The host system 20 is a system that manages and controls a manufacturing line including, for example, continuous casting and hot rolling, and may be configured, for example, by a computer different from the distribution plan creation device 10. The distribution plan creation device 10 acquires various data including input information described below, which is necessary for calculations for creating a distribution plan (such as simulations and charging temperature predictions using models), from the host system 20. In addition, the distribution plan creation device 10 outputs the results of the calculations including the created distribution plan to the host system 20.

[0026] The acquisition unit 11 acquires input information. The input information includes inventory information, casting information, heating information, rolling information, and equipment information. The inventory information includes information on slabs in the slab yard and slabs in motion. The inventory information includes initial inventory information, which is information on inventory in the slab yard when the calculation unit 12 starts calculation for creating a logistics plan. The casting information includes information on the dimensions and steel type of the cast slab. The casting information also includes information on the surface temperature of the slab at the time of torch cutting. The surface temperature at the time of torch cutting is the surface temperature of a slab that has been cast by a continuous casting machine and torch cut to a predetermined length, and may be referred to as the surface temperature when extracted from the continuous casting machine. The heating information includes the slab in the heating furnace and the heating status. The heating information includes a scheduled time of charging into the heating furnace, which constitutes a part of the charging plan for the heating furnace. The heating information may also include the actual temperature value (actual temperature measurement value) of the slab before charging into the heating furnace. The rolling information includes a rolling plan and rolling constraints. The charging plan for the heating furnace may constitute a part of the rolling plan. The rolling plan may be created by, for example, the host system 20 using a known planning method. The distribution plan creation device 10 extracts a charging plan for the heating furnace from the rolling plan, etc., and creates a distribution plan for the slab yard in accordance with the charging plan for the heating furnace and so that the slab yard is operable. The equipment information includes the presence or absence of failures of each piece of equipment, equipment capacity specifications, and storage space constraints.

[0027] The calculation unit 12 receives input information from the acquisition unit 11 and executes calculations for creating a logistics plan. In this embodiment, the calculations include a logistics simulation. The calculation unit 12 may execute a process of determining whether an operation is feasible through the simulation and excluding candidates determined to be unfeasible. The functional unit of the calculation unit 12 that executes the simulation may be referred to as a simulator unit (logistics simulator). The calculations executed by the calculation unit 12 are not limited to simulations, and may execute various calculations, judgments, and decisions related to the creation of a logistics plan. In this embodiment, the calculation unit 12 predicts the slab charging temperature using a model (calculation formula). In addition, the calculation unit 12 can execute optimization, which will be described later. The functional unit of the calculation unit 12 that performs calculations and optimization using a model may be referred to as a model unit.

[0028] The output unit 13 outputs the results of the calculation by the calculation unit 12. The results include a logistics plan selected from the candidates created by the calculation unit 12. By receiving the logistics plan selected by the calculation unit 12 and controlling the production line in accordance with the logistics plan, it is possible to improve production efficiency and achieve energy savings by suppressing heat radiation.

[0029] The outline of the processing in the calculation unit 12 will be described below. The model unit creates candidates for the logistics plan in the yard at a timing corresponding to an event (e.g., a change in plan) or at a regular timing. Here, the estimated slab arrival time in front of the slab yard and the limit time of the yard discharge are defined. The estimated slab arrival time in front of the slab yard is a value calculated using past results, etc., for the time when the slab arrives in front of the slab yard via the traverse car. The limit time of the yard discharge is a value calculated using past results, etc., for the discharge time of the slab that is in time for the scheduled charging time into the heating furnace. In detail, the limit time of the yard discharge is calculated from the lead time from the discharge command to the arrival in front of the heating furnace, such as past results. The model unit passes the created candidates for the logistics plan to the simulator unit. Here, it is preferable that the model unit passes the candidates for the logistics plan to the simulator unit after removing candidates that are obviously infeasible.

[0030] The simulator unit performs a logistics simulation for each of the candidates for the delivered logistics plan using a discrete event model based on processing logic including queues. The simulator unit excludes from the candidate group of logistics plan candidates including relocation within the yard, plans that are deemed infeasible, such as plans in which the relocation cannot be completed in time for the scheduled charging time or plans that do not have sufficient crane operating capacity. In addition, the simulator unit simulates the arrival time of non-direct delivery materials at the slab yard based on the torch cut time and equipment specification values, calculates the piles that can be received at the time of arrival, and creates a group of candidates for the number of possible piles. The simulator unit corrects the implementation time of each transport command in the candidate group to match the simulation results, and sets them as feasible plan candidates. The feasible plan candidates are sent to the model unit.

[0031] The model unit updates the temperature transition according to the corrected transportation time and predicts the charging temperature. The model unit sets a function (evaluation formula) related to charging for each slab temperature obtained from the charging temperature prediction, performs optimization calculation, and finally selects one logistics plan and outputs it via the output unit 13.

[0032] The details of the process are described below. The model unit may create candidates for the distribution plan at the timing corresponding to the estimated arrival time of each slab in front of the slab yard, the scheduled time for charging each slab into the heating furnace, when updating the charging plan, when determining whether the slab after casting is a direct delivery or non-direct delivery material, at a certain interval, etc. The distribution plan includes a pattern of redistribution for charging. Slabs stacked above the charging target (hereinafter, the redistribution target material) to be moved by redistribution may be returned to the original pile after the movement, or may remain moved. In creating the redistribution in the candidate for the distribution plan, the crane grab constraint and the pile constraint are taken into consideration. Figure 5 shows the process of determining the candidate for the distribution plan, and the redistribution candidate is also created according to the flow of Figure 5. In the process of creating the handling candidate group in Figure 5, the model unit reads the specification values ​​of the crane constraint given by the input information, and creates what combinations the crane can move for the redistribution target material according to Figure 6. The crane gripping width difference constraint and the crane gripping length difference constraint are crane gripping constraints on the width difference and length difference of the slab, respectively, which will be described later. The crane gripping weight constraint is a crane gripping constraint on the weight of the slab. The crane gripping thickness constraint is a crane gripping constraint on the thickness of the slab. The model unit creates a handling candidate group (i.e., a candidate group of materials to be replaced and devices to transport the materials to be replaced) that satisfies all of these constraints. At this stage, the order of replacement is not taken into consideration, and the handling candidate group is created from the perspective of how many sheets of materials to be replaced are transported at the same time. The model unit executes the process of FIG. 7 for the created candidate group. In other words, the model unit creates candidates for the order of replacement and narrows down the piles that can be replaced by various constraints. When there are multiple piles to be replaced, the model unit may select the closest pile from among them. This makes it possible to locally minimize the handling load. The various constraints include width and length constraints when stacking the piles. For example, the slab width difference is calculated by dividing the slab width ω 1 and the smallest width slab ω among the lower slabs 2 Compared with ω 1 -ω 2The width constraint is that this difference in width must be equal to or less than the maximum allowable difference. There is a similar constraint for the length. Also, there is a constraint that the height must be equal to or less than the maximum allowable height. The model reads the criteria table for stacking constraints (see Figure 15) and narrows down the candidates for re-allocation while taking the stacking constraints into account.

[0033] The simulator unit performs a simulation of the rearrangement for each pattern sent from the model unit, and verifies the delivery of materials to be charged in accordance with the charging time. The simulator unit eliminates candidates for logistics plans in which the delivery of materials to be charged will not be in time. In addition, to determine which piles can be accepted, the simulator unit performs a simulation taking into account the arrival times of non-direct shipment materials, and eliminates plan candidates that do not have acceptable piles, as shown in Figure 9. The simulator unit sets the group of candidates narrowed down by this process as feasible plan candidates. In addition, the simulator unit determines the predicted implementation time of the transport order for each plan based on the simulation results. The feasible plan candidates, including the determined predicted implementation times of the transport order, are sent to the model unit.

[0034] The model unit performs a prediction calculation of the charging temperature for the feasible plan candidates from the simulator unit using the following equations (1) to (3).

[0035]

number

[0036] Here, T t is the slab surface temperature at time t (℃). T t-τ is the slab surface temperature (℃) at time t-τ. Δτ is the interval (seconds) for updating the temperature. C p is the specific heat of the slab (kJ / (kg K)). W is the weight of the slab (kg). S is the surface area of ​​the slab (m 2 ) θ is the slab surface temperature (℃). T a is the slab ambient temperature (℃). σ is the Stefan-Boltzmann constant (5.67×10 -8 E / (m 2 K4 )). ε is the emissivity of the slab. α is the heat transfer coefficient between the slab and the atmosphere (W / (m 2 where D is the slab thickness (m). λ is the thermal conductivity (W / (mK)). T is the cross-sectional average temperature of the slab (℃).

[0037] Equations (1) and (2) are heat dissipation model equations for a slab, and are obtained based on the equation for steady heat conduction, Fourier's law, or Newton's cooling theory. Equation (3) is an equation for converting the slab surface temperature to the average cross-sectional temperature. Here, it is assumed that there is no heat transfer between slabs, and the surface area S of the slab changes as shown in Figures 4A to 4C depending on the position of the stacked slab. Figure 4A shows that the slab is located in the middle of the pile in the yard, and heat transfers from the four horizontal sides. D is the length of the slab in the direction in which the slabs are stacked in the pile (height direction), and is the above-mentioned slab thickness. W is the length in the width direction of the slab, that is, the slab width. L is the length of the slab in the height direction and the direction perpendicular to the width direction, that is, the slab length. Figure 4B shows that the slab is located in the top or bottom of the pile in the yard, and heat transfers from a total of five sides, that is, the four horizontal sides and one side above or below. Figure 4C shows that there is only one slab in the yard, forming a pile, with heat transferring from a total of six sides: four lateral sides and two top and bottom sides.

[0038] In addition, the slab atmosphere temperature (T a ) is given as a constant, such as an estimated value based on past performance data. The specific heat of the slab (C p ) and thermal conductivity (λ) are generally known to depend on the carbon concentration and temperature. p ) and thermal conductivity (λ) are determined using a physical property table compiled for each carbon concentration of the slab, as shown in FIG. 12. Here, the specific heat (C p ) and thermal conductivity (λ) may be determined by linear interpolation for temperatures or carbon concentrations not found in the physical property table. 1 , T 2 If the weighting factor q used in the interpolation is 1 , q 2is carried out using equations (4) and (5).

[0039]

number

[0040] Also, a table like that in Figure 12 shows the carbon concentration c A , c B , c C If exists for p A , p B , p C is expressed as shown in Fig. 13. For example, for thermal conductivity λ, the interpolation formula is expressed as (6).

[0041]

number

[0042] Here, λ n1 (n=A, B, C) is the carbon concentration c n In the table, temperature T 1 is the thermal conductivity value of . n2 (n=A, B, C) is the carbon concentration c n In the table, temperature T 2 is the thermal conductivity value.

[0043] The model unit performs optimization using the following formula (7). Here, formula (7) takes into consideration the maximization of the charging temperature of each slab and the reduction of the load of the combustion control. In other words, selecting a logistics plan that takes the maximum or minimum value (the minimum value in this embodiment) in an evaluation formula such as formula (7) corresponds to considering the heating constraint. Here, when using the evaluation formula of formula (7), a quasi-optimal solution may be evaluated as the optimal value. In other words, it does not necessarily have to be the maximum or minimum value, but it may be evaluated as the optimal value if the value of the evaluation formula is close to the maximum value or close to the minimum value. In this way, the heating constraint is taken into consideration by selecting a logistics plan based on the evaluation formula. The first term of formula (7) is a term related to the reduction of the difference in charging temperature of adjacent slabs in the heating furnace. Also, the second term of formula (7) is a term related to the improvement of the charging temperature of the slab to the heating furnace.

[0044]

number

[0045] Here, I represents a set of slabs whose charging order has already been determined. i is the charging temperature of slab "i" (℃). Since the optimization calculates all slab temperatures for each logistics plan, the best solution can be derived by exhaustive search. With the above method, it is possible to select an optimized charging plan while taking logistics into consideration. This makes it possible to improve production efficiency and save energy by suppressing heat radiation.

[0046] The effects of the present disclosure will be specifically described below based on examples, but the present disclosure is not limited to these examples.

[0047] FIG. 14 shows equipment specification values. FIG. 15 shows a reference table for stacking constraints in the slab yard. FIG. 16 shows slab attributes and slab position information placed in the slab yard when input information is read. FIG. 16 corresponds to the initial inventory information in the slab yard. Slabs are distinguished by slab numbers, which are numbers unique to each slab. For example, a slab with slab number 5 is written as slab "5". There are a maximum of seven piles in the slab yard, and the pile names (pile names) are defined as D1, D2, ..., D7 from the traverse cart side. A maximum of nine slabs are stacked in one pile. The bottom layer of the slabs in a pile is numbered 1. For example, the third slab from the bottom in a pile has a stacking order of 3. The ambient temperature (slab ambient temperature) in the slab yard is set to 700°C. The transport tables are managed in a divided manner, and the divided transport tables have names such as TB1, TB2, ...

[0048] FIG. 17 shows information about the scheduled casting plan at 8:00:00 on August 1, 2021, which is the starting point of the plan update. FIG. 18 shows information about the charging plan at the starting point of the plan update. Direct delivery materials are indicated as "direct" in the Direct Delivery Materials (Direct / Non-Direct) column. Non-direct delivery materials are indicated as "non-direct" in the Direct Delivery Materials (Direct / Non-Direct) column. The logistics plan may be updated at the timing of the estimated slab yard arrival time obtained from the casting information and charging information, and the yard discharge time limit time of each slab to be charged in the slab yard. In this embodiment, it is assumed that the charging plan was updated to FIG. 19 at 8:05:00 on August 1, 2021. For the charging plan in FIG. 19, the slab that needs to be redistributed is slab "4" from FIG. 16. In other words, slab "4" has a stacking order of 1 and is located at the bottom of the pile (D2). The materials to be replaced are the other slabs "5" and "6" stacked in pile (D2). Based on the crane constraints in Fig. 14, handling is created to transport slab "5" after transporting slab "6" as in P1 and P2 in Fig. 20. In addition, a total of three handling patterns are created, including handling to transport slabs "5" and "6" simultaneously as in P3 in Fig. 20.

[0049] Next, transfer candidates that can be transported from each handling are created. The piles that can be transferred are narrowed down from the pile standard value table, and for each handling command, the pile with the shortest transfer distance is selected from among the piles that can be transferred. Here, in Figure 10, "D" corresponds to slab "4", "E" corresponds to slab "5", and "F" corresponds to slab "6".

[0050] The simulator executes a logistics simulation for three patterns. For P1 and P2, non-direct shipment materials are accepted by D4, and a logistics plan candidate including the transportation command predicted implementation time in Figures 21 and 22 is obtained. For P3, the logistics plan candidate in Figure 23 is obtained. Here, P2 was deleted from the candidates because the discharge of slab "5" was not in time for the scheduled charging time. Therefore, P1 and P3 were treated as feasible plan candidates. The model predicted the charging temperature for P1 and P3. Calculations were performed using the above formula and the physical property value table in Figure 24, and the charging plan information in Figures 25 and 26 was obtained for P1 and P3. By the optimization calculation using formula (7), the evaluation value of P1 was calculated using formula (8). The evaluation value of P3 was calculated using formula (9).

[0051]

number

[0052] As a result, the P3 logistics plan was selected. Here, by feeding back the temperature actually measured at the time of charging to the predicted charging temperature, the formulas for calculating the charging temperature prediction from the predicted value and actual value can be expressed as formulas (10) and (11).

[0053]

number

[0054] Here, c means the correction of the prediction error obtained from the difference between the predicted temperature and the measured temperature. t (min) is the lead time from the torch cutting time to the actual measurement time of the charging temperature. α and β are parameters related to the regression equation of the prediction error obtained from each slab. These parameters can be obtained by the least squares method or the like. In other words, equations (10) and (11) are regression equations based on the residence time of the slab from after casting or extraction from the heating furnace until it is charged into the heating furnace, and the difference between the predicted value of the slab temperature in the simulation and the actual value of the slab temperature when it is charged into the heating furnace.

[0055] When the best solution in FIG. 26 was executed, the actual charging temperature value in FIG. 27 was obtained. At this time, α = -0.0646 and β = 4.0096 were obtained. By correcting the charging temperature using equations (10) and (11), the error in the predicted temperature can be reduced. When the error between the predicted charging temperature value and the actual charging temperature value before and after the application of this correction formula was compared over a long period of time (2021 / 11 / 15 to 2022 / 1 / 4), it was confirmed that the RMSE was improved by applying the correction formula, as shown in FIG. 11. The horizontal axis of FIG. 11 is t (min). The vertical axis of FIG. 11 is the error (℃) between the predicted charging temperature value and the actual charging temperature value. The left figure in FIG. 11 shows the case where the correction formula is not applied, and the right figure shows the case where the correction formula is applied.

[0056] As described above, the logistics plan creation method and logistics plan creation device 10 according to this embodiment can create a logistics plan that contributes to energy saving while ensuring the feasibility of operations through the above-mentioned steps and configuration. By following a logistics plan that ensures the feasibility of operations including multi-stage processes through verification using logistics simulation, it is possible to avoid adjustments to execution due to bottlenecks in logistics capacity during actual operations, enabling efficient manufacturing of steel products. In addition, a logistics plan is selected from multiple candidates to improve the charging temperature into the heating furnace and provide a heating pattern operation of the heating furnace with a light load, thereby realizing energy saving.

[0057] Although the embodiments of the present disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art can easily make various modifications or corrections based on the present disclosure. Therefore, it should be noted that these modifications or corrections are included in the scope of the present disclosure. For example, the functions included in each component or each step can be rearranged so as not to be logically inconsistent, and multiple components or steps can be combined into one or divided. The embodiments of the present disclosure can also be realized as a program executed by a processor included in the device or a storage medium on which a program is recorded. It should be understood that these are also included in the scope of the present disclosure. [Explanation of symbols]

[0058] 10. Logistics planning device 11 Acquisition Department 12 Arithmetic section 13 Output section 20 Upper System

Claims

1. A method for creating a logistics plan for a slab yard that stores slabs to be extracted from a continuous casting machine and charged into a heating furnace or slabs to be extracted from the heating furnace and charged into the heating furnace again without being hot rolled, in a rolling process in which a slab is hot rolled by a hot rolling mill, comprising: obtaining input information including inventory information, casting information, heat information, rolling information, and equipment information; updating the charging temperature of the slab into the heating furnace based on the surface temperature of the slab when extracted from the continuous casting machine, which is included in the casting information, initial inventory information at the slab yard, which is included in the inventory information, and equipment specification values ​​between the steelmaking process by the continuous casting machine and the rolling process, which are included in the equipment information; Creating a candidate logistics plan for the slab yard based on the heating information and the charging plan for the heating furnace included in the rolling information; A method for creating a logistics plan comprising: running a simulation for the candidate logistics plans and selecting a logistics plan taking into account the heating constraint.

2. The logistics planning method of claim 1, wherein the heating constraint is taken into consideration by selecting a logistics plan based on an evaluation equation including a term relating to improving the charging temperature of the slab into the heating furnace and a term relating to reducing the difference in charging temperatures of adjacent slabs in the heating furnace.

3. The method for creating a logistics plan according to claim 1 or 2, wherein creating the candidate logistics plan is performed taking into account crane grab constraints and pile constraints.

4. 3. The method for creating a logistics plan according to claim 1, wherein the charging temperature is updated by predicting the charging temperature of the slab into the heating furnace using a slab heat dissipation model equation that assumes that there is no heat transfer between slabs.

5. The logistics plan creation method according to claim 4, wherein the heat dissipation model equation is a regression equation based on the residence time until the slab is charged into the heating furnace and the difference between the predicted slab temperature and the actual slab temperature at the time of charging into the heating furnace.

6. A logistics plan creation device that creates a logistics plan for a slab yard that stores slabs that are extracted from a continuous casting machine and charged into a heating furnace, or slabs that are extracted from the heating furnace and then charged into the heating furnace again without being hot rolled, in a rolling process in which a slab is hot rolled by a hot rolling mill, comprising: An acquisition unit that acquires input information including inventory information, casting information, heating information, rolling information, and equipment information; updating the charging temperature of the slab into the heating furnace based on the surface temperature of the slab when extracted from the continuous casting machine, which is included in the casting information, the initial inventory information at the slab yard, which is included in the inventory information, and the equipment specification values ​​between the steelmaking process by the continuous casting machine and the rolling process, which are included in the equipment information; A candidate physical distribution plan for the slab yard is created based on the heating information and the charging plan for the heating furnace included in the rolling information; A logistics plan creation device comprising: a calculation unit that executes a simulation for the candidates for the logistics plan and selects a logistics plan taking into account heating constraints.

7. The logistics plan creation device of claim 6, wherein the heating constraint is taken into consideration by selecting a logistics plan based on an evaluation formula including a term relating to improving the charging temperature of the slab into the heating furnace and a term relating to reducing the difference in charging temperatures of adjacent slabs in the heating furnace.

8. The logistics plan creation device according to claim 6 or 7, wherein creating the candidate logistics plan is performed taking into account crane grab constraints and pile constraints.

9. 8. The logistics plan creation device according to claim 6, wherein the charging temperature is updated by predicting the charging temperature of the slab into the heating furnace using a slab heat dissipation model equation that assumes that there is no heat transfer between slabs.

10. The logistics plan creation device according to claim 9, wherein the heat dissipation model equation is a regression equation based on a residence time until the slab is charged into the heating furnace and a difference between a predicted slab temperature value and an actual slab temperature value at the time of charging into the heating furnace.

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