Double-machine set batch production optimization control method
By optimizing the dual-machine batch production control method, the problems of low production efficiency and unstable product quality were solved, resulting in increased production capacity and consistent quality, while reducing production changeover losses and energy consumption.
Patent Information
- Application Number
- CN202511320534.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-16
- Publication Date
- 2025-11-28
AI Technical Summary
The existing dual-machine batch production method suffers from low production efficiency, high cost, and unstable product quality.
By optimizing control methods, including pre-planning of steelmaking production, logistics time optimization models, dual-machine collaborative working modes, and genetic algorithm solutions, efficient scheduling of steel slabs and optimization of production parameters are achieved, thereby improving equipment utilization and production efficiency.
It achieved a 1.15-1.7 times increase in production capacity, reduced production changeover losses and the frequency of temperature increases and decreases, and improved product quality consistency and production efficiency.
Smart Images
Figure CN121017265A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to hot rolling production technology, in particular to a double-machine batch production optimization control method. BACKGROUND
[0002] The double-machine batch production method is a way of using two machines to produce in batches in the production process, aiming to improve production efficiency, reduce costs and improve product quality. Hot rolling double-machine batch production is a mode of efficient batch rolling achieved by optimizing equipment configuration and production rhythm, the core of which is to use the coordinated work of the rough rolling and finishing rolling two groups of rolling mills to concentrate the processing of the same type or same specification slabs, reduce the conversion loss and improve the efficiency, especially suitable for high value-added products such as thin gauge. SUMMARY
[0003] The technical problem to be solved by the present application is to provide a double-machine batch production optimization control method to solve the defects in the prior art.
[0004] The technical scheme adopted by the present application to solve the technical problem is: a double-machine batch production optimization control method, comprising the following steps: 1) According to the variety structure rolling demand plan transmitted by the hot rolling plant, pre-arrange the steel production plan; The rolling demand plan includes the specification, quantity and rolling period of the product; 2) Based on the logistics time optimization model, quickly transport the slabs from the steelmaking process to the hot rolling process; 2.1) Based on the production plan, group the same specification target steel slab into multiple batches, i.e. the i-th batch of target steel slab is rolled and processed in the k-th period of the j-th unit, denoted as x ijk ; 2.2) Establish a logistics time optimization model to obtain the scheduling strategy of the target steel slab by batch; Wherein, the logistics time optimization model, the optimization goal is to minimize the inventory cost and transportation time, and the output is the distribution information related to the logistics state of the target steel and the hot rolling process arrival time of the target steel slab; 3) Solve the model, if the hot rolling process arrival time of the target steel slab does not meet the rolling demand plan, go to step 4); 4) According to the hot rolling process arrival time of the target steel slab, adopt the rough rolling-finishing rolling double-machine collaborative work mode in the rolling process, optimize the scheduling to improve the furnace temperature and maximize the continuous rolling unit, so as to improve the efficiency of the hot rolling process; 5) Generate the optimal scheduling sequence according to the optimization scheduling result as the production scheduling strategy.
[0005] According to the above scheme, in step 2), the logistics time optimization model is as follows:
[0006] wherein w1 is the delay delivery cost weight, w2 is the target steel slab advance to rolling process inventory cost weight, is the completion processing time of the i-th batch, I i is the target steel slab advance to rolling process inventory cost, d i is the delivery time; n is the total number of batches scheduled; According to the above scheme, in the step 2), The logistics time optimization model is constrained as follows: ; wherein, is the unit processing time of the j-th machine group, is the k-period device processing capacity, = 1 indicates that the batch i is allocated to the k-period processing of the machine group j; = 0 indicates that the batch i is not allocated to the k-period processing of the machine group j; is the steelmaking process completion time of the batch i; is the rolling process start time of the batch i, is the logistics time of the batch i, is the necessary time for process conversion; According to the above scheme, in the step 4), the in-furnace temperature is improved and the maximum continuous rolling unit is maximized by the scheduling optimization model, which is as follows: The input of the model is the task set J = {J1, J2, …, J n}, the completion of a single task J i requires the processing time p j of the machine group j; the output is the task start time sequence: [S1, S2, …, S n ] and the batch processing grouping ; wherein the task set is arranged according to the hot rolling process arrival time of the target steel slab batch; the batch processing grouping indicates that multiple single tasks J i are continuously rolled, indicates the k-period; The model is: min (C max , T max ); ; ; wherein C max is the manufacturing span, T max is the maximum delay time.
[0007] According to the above scheme, the equipment constraints of the scheduling optimization model are as follows: In the batch processing group, the quantity of the steel billets of each group of multiple single tasks does not exceed the maximum loading capacity of the heating furnace; The unit includes a rough rolling and a finishing rolling; Equipment utilization rate ≥85%; Wherein, M is the total number of units.
[0008] According to the above scheme, the process constraints of the scheduling optimization model are as follows: The time from the steel plate billet output of each task to entering the heating furnace is not more than 2 hours; The temperature T of the steel plate billet entering the heating furnace of each task is not less than 700 degrees.
[0009] According to the above scheme, the process constraints of the scheduling optimization model also include that the number of single tasks in the batch processing group is greater than or equal to 14.
[0010] According to the above scheme, the scheduling optimization model is solved by using a genetic algorithm.
[0011] The present application also provides an electronic device, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of the above schemes.
[0012] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method according to any one of the above schemes.
[0013] The present application has the following beneficial effects: 1. The present application organizes smelting plan pre-scheduling in advance according to the variety structure, realizes the scheduling scheme of reducing the conversion loss and improving the efficiency and quality stability by concentrating the processing of the same specification plate billets; 2. The present application adjusts the rolling scheduling sequence according to the steelmaking batch smelting condition, improves the overall ingot temperature of the variety, realizes continuous production and maximizes the continuous rolling unit number, and optimizes the production efficiency.
[0014] 3. The present application adopts a double-machine batch production organization mode, which can improve the production capacity to 1.15-1.7 times of a single machine by double-machine cooperation, and can reduce the temperature rising and falling frequency, improve the product quality consistency, and effectively improve the production efficiency in combination with the rolling scheduling scheme. BRIEF DESCRIPTION OF DRAWINGS
[0015] The present application will be further described below in combination with the drawings and examples, and the drawings are as follows: Figure 1is a method flowchart of an embodiment of the present application. DETAILED DESCRIPTION
[0016] In order to make the objects, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.
[0017] As shown in Figure 1 , a double-machine batch production optimization control method includes the following steps: 1) According to the variety structure rolling demand plan transmitted from the hot rolling plant, pre-arrange the steelmaking production plan; The rolling demand plan includes the specifications, quantity and rolling time period of the products; 2) Based on a logistics time optimization model, transport the slabs from the steelmaking process to the hot rolling process; 2.1) Based on the steelmaking production plan, group the same-specification target steel slabs into multiple batches to obtain all target steel slab batches, assuming that the i-th batch of target steel slabs is rolled in the k-th time period of the j-th machine group, denoted as x ijk ; 2.2) Establish a logistics time optimization model to obtain the scheduling strategy of the target steel slabs by batch; Wherein, the logistics time optimization model, the optimization goal is to minimize the inventory cost and transportation time, the output is the distribution information related to the logistics state of the target steel and the hot rolling process arrival time of the target steel slabs; The model is as follows:
[0018] Wherein, w1 is the delay delivery cost weight, w2 is the inventory cost weight of the target steel slabs arriving at the rolling process in advance, is the time for the i-th batch of steel slabs to complete rolling processing, I i is the inventory cost of the i-th batch of target steel slabs arriving at the rolling process in advance, d i is the delivery time, determined according to the rolling demand plan; n is the total number of batches scheduled; =F( , , , ) The constraints are as follows: ; Wherein, is the unit processing time of the j-th machine group, is the k-time period device processing capacity, =1 indicates that the batch i is allocated to the k period of the machine group j for processing; =0 indicates that the batch i is not allocated to the k period of the machine group j for processing; is the steelmaking process completion time of the batch i; is the rolling process start time of the batch i, is the logistics time of the batch i, is the necessary time of process conversion; 3) solving the model, if the hot rolling process arrival time of the target steel plate blank does not meet the rolling demand plan, go to step 4); if the hot rolling process arrival time of the target steel plate blank meets the rolling demand plan, then arrange production according to the original rolling demand plan; 4) according to the hot rolling process arrival time of the target steel plate blank, adopt the rough rolling-finish rolling double-machine collaborative working mode in the rolling process, optimize the scheduling to improve the entry temperature and maximize the continuous rolling unit, so as to improve the efficiency of the hot rolling process; In this embodiment, the original single-machine mode is replaced by the rough rolling-finish rolling double-machine mode, so as to realize the original rolling production plan.
[0019] Through the double-machine collaborative working, the heating, rough rolling and finish rolling production process parameters are flexibly adjusted according to the production demand, the batch production of varieties is realized, and the process waiting for heating and temperature rising caused by cross scheduling can be effectively reduced. Table 1 is a comparison of production process parameters between the double-machine batch mode and the single-machine mode.
[0020] Table 1 Comparison of production process parameters between double-machine batch mode and single-machine mode
[0021] As can be seen from Table 1, the double-machine collaboration can increase the production capacity to more than 1.15 times of the single-machine frame, while reducing the temperature rising and falling frequency, and the fuel consumption loss is significantly reduced. The single energy consumption loss is 15.2 yuan / ton of steel, and in terms of quality, due to the reduction of temperature fluctuation and interface interference in the rolling process, the finish rolling can adopt a centralized temperature compensation heating system to ensure the isothermal heating of the rolled piece, thereby improving the product consistency.
[0022] The scheduling optimization model used in scheduling optimization is as follows: The input of the model is a task set J={J1, J2, …, J n}, and the processing time p i required by the machine group j; j The output is a task start time sequence: [S1, S2, …, S n ] and batch processing grouping ; The task set is arranged according to the hot rolling process arrival time of the target steel plate blank batch; the batch processing grouping indicates that multiple single tasks J i are continuously rolled, denotes the k period; The model is: min(C max , T max ); ; ; wherein C max is the manufacturing span, and T max is the maximum hysteresis time.
[0023] The equipment constraints of the scheduling optimization model are as follows: In the batch processing group, the amount of steel billets of multiple single tasks in each group does not exceed the maximum charging capacity of the heating furnace; The unit set M={M1, M2} includes the rough rolling M1 and the finishing rolling M2; The equipment utilization rate is greater than or equal to 85%; wherein, M is the total number of units.
[0024] The process constraints of the scheduling optimization model are as follows: The time from the production of steel plates to the entry of the heating furnace of each task is not more than 2 hours; The charging temperature is related to the production efficiency, and the production efficiency is improved by 3% for every 10℃ increase in temperature. We control the temperature T of the steel plates entering the heating furnace of each task to be not less than 700 degrees; The number of single tasks in the batch processing group is greater than or equal to 14.
[0025] 5) According to the optimal scheduling result, the optimal scheduling sequence is generated as the production scheduling strategy. The scheduling optimization model is solved by using the genetic algorithm, wherein, Genetic algorithm coding: Chromosome structure: double-layer coding mechanism is adopted: Outer gene=[task order|unit allocation]; Inner gene=[S1, S2, ……S n ]; The fitness function is as follows: .
[0026] It should be understood that those skilled in the art can make improvements or changes according to the above description, and all these improvements and changes shall belong to the protection scope of the appended claims of the present application.
Claims
1. A dual-machine batch production optimization control method, characterized in that, Includes the following steps: 1) Based on the product rolling demand plan from the hot rolling mill, pre-plan the steelmaking production schedule; The rolling demand plan includes the product specifications, quantity, and rolling time period; 2) Based on the logistics time optimization model, quickly transport slabs from the steelmaking process to the hot rolling process; 2.1) Based on the production plan, target steel slabs of the same specification are grouped into multiple batches, that is, the i-th batch of target steel slabs is rolled and processed in the k-th time period of the j-th unit, denoted as x. ijk ;; 2.2) Establish a logistics time optimization model to obtain the batch scheduling strategy for the target steel slabs; Among them, the logistics time optimization model aims to minimize inventory costs and delivery time, and outputs the delivery information related to the logistics status of the target steel and the arrival time of the hot rolling process of the target steel slab. 3) Solve the model. If the arrival time of the hot rolling process of the target steel slab does not meet the rolling demand plan, proceed to step 4). 4) Based on the arrival time of the hot rolling process of the target steel slab, the steel rolling process adopts a roughing-finishing dual-machine collaborative working mode. By optimizing the scheduling, the furnace temperature is increased and the continuous rolling unit is maximized to improve the efficiency of the hot rolling process. 5) Generate the optimal scheduling sequence based on the optimized scheduling results, which will serve as the production scheduling strategy.
2. The dual-machine batch production optimization control method according to claim 1, characterized in that, In step 2), the logistics time optimization model is as follows: Where w1 is the delayed delivery cost weight, and w2 is the inventory cost weight of the target steel slab brought forward to the rolling process. Let I be the time for the i-th batch to complete processing. i For inventory costs, d i is the delivery time; n is the total number of batches scheduled.
3. The dual-machine batch production optimization control method according to claim 2, characterized in that, In step 2), The constraints of the logistics time optimization model are as follows: ； in, The unit processing time of the j-th unit. Let k be the processing capacity of the equipment during time period k. =l indicates that batch i is assigned to be processed in time period k of unit j; =0 indicates that batch i was not assigned to be processed in time slot k of unit j; The completion time of the steelmaking process for batch i; The start time of the rolling process for batch i. For batch i, the logistics time is... This is the necessary time for process changeover.
4. The dual-machine batch production optimization control method according to claim 1, characterized in that, In step 4), the furnace inlet temperature is increased and the continuous rolling unit is maximized through a scheduling optimization model. The scheduling optimization model is as follows: The model's input is a task set J = {J1, J2, ..., J...} n }, complete single task J i The processing time p required for unit j j The output is the task start time sequence: [S1, S2, ..., S...] n ], and batch processing groups ; The task set is arranged according to the arrival time of the hot rolling process of the target steel slab batch; Batch processing groups represent multiple single tasks J i Continuous rolling, Indicates time period k; The model is: min(C max ,T max ); ； ； Among them, C max To create the span, T max This represents the maximum hysteresis time.
5. The dual-machine batch production optimization control method according to claim 4, characterized in that, The equipment constraints of the scheduling optimization model are as follows: In batch processing groups, the amount of billets in each group of multiple single tasks does not exceed the maximum loading capacity of the heating furnace; The unit includes two mills: a roughing mill and a finishing mill. Equipment utilization rate ≥85%; in, M represents the total number of generating units.
6. The dual-machine batch production optimization control method according to claim 4, characterized in that, The process constraints of the scheduling optimization model are as follows: The time from the steel slab output to its entry into the heating furnace for each task shall not exceed 2 hours; The temperature T of the steel slabs entering the heating furnace for each task shall not be lower than 700 degrees.
7. The dual-machine batch production optimization control method according to claim 5, characterized in that, The process constraints of the scheduling optimization model also include that the number of individual tasks in the batch group is greater than or equal to 14.
8. The dual-machine batch production optimization control method according to claim 4, characterized in that, The scheduling optimization model is solved using a genetic algorithm.
9. An electronic device, characterized in that, include: One or more processors; as well as Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 8.