A timing compilation method for automatic feeding of an optimized mixing and batching bin

Abstract

The application discloses a kind of based on optimization's mix uniform batching bin automatic feeding timing compilation method, belong to metallurgical automation technical field, including: obtaining the parameter information and state information of system, and according to actual equipment state and parameter establishes corresponding mathematical model, predicts the system state of subsequent multiple task periods, to design with the weight of raw material in bin as constraint condition, with the optimal feeding efficiency as performance index, the automatic feeding timing compilation optimization problem of bin, and through the design genetic algorithm and dual simplex method, the feeding sequence of bin and the start time and end time of each feeding task are solved, to complete the compilation of automatic feeding timing.The method provided by the application can effectively avoid the empty bin problem that may occur in the process of mix uniform batching bin automatic feeding, improve the running efficiency in mix uniform process.

Description

Technical Field

[0001] This invention belongs to the field of metallurgical automation technology, and more specifically, relates to a timing programming method for automatic feeding of a mixing and batching bin based on optimization. Background Technology

[0002] Due to the diversity of mineral resources in my country, steel enterprises typically need to uniformly mix a dozen or even dozens of different types of raw materials in a certain proportion to meet the quality requirements of sinter. The raw material mixing and batching process involves two steps: the first is the material handling operation in the primary material yard, where the corresponding raw materials are extracted and transported by conveyor belt to the mixing and batching tank for temporary storage; the second step involves the mixing and batching tank using a disc feeder set to different feeding speeds according to the formula ratio, feeding the raw materials from the batching tank onto the same conveyor belt, which then transports the ore raw materials to the stockpile in the mixing and batching yard to form a mixed stockpile. The mixing and batching tank generally contains multiple silos, each equipped with 1-2 feeding trolleys to supply materials to the various silos, and each feeding trolley corresponds to 1-2 reclaimers. Because the mixing ratio varies, the feeding speed of the raw materials in each silo also differs. Therefore, how to rationally schedule the feeding trolleys and reclaimers to minimize energy consumption while ensuring that each silo is not empty is key to improving the operating efficiency of the raw material yard.

[0003] Currently, steel companies mostly use manual timing to supply materials to various silos in their raw material yards. This method relies on human experience, which can easily lead to untimely scheduling, empty silos resulting in significant discrepancies between the mixed composition and expectations, and makes it difficult to control operating costs. In recent years, some companies have used automated methods to calculate feeding sequences, but these methods cannot guarantee that empty silos will not occur, nor do they consider the operating efficiency of the feeding trolleys and reclaimers. There is still considerable room for improvement in this area.

[0004] In summary, developing a method for programming the automatic feeding sequence of mixing silos to avoid empty silos, ensure the quality of raw material mixing, and improve the operating efficiency of the automatic feeding process is a key step in further improving the energy efficiency and production level of mixing yards. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention proposes a timing-based method for automatic feeding of a mixing and batching silo. By establishing a mathematical model for mixing and batching, the system state of multiple subsequent task cycles is predicted. This establishes an automatic feeding timing optimization problem with the weight of raw materials in the silo as a constraint and optimal feeding efficiency as a performance indicator. Finally, the method solves for an automatic feeding timing that meets the requirements.

[0006] To achieve the above objectives, the present invention provides a timing-based method for automatic feeding of a mixing and batching silo, comprising:

[0007] Obtain parameter and status information of the mixing silo system, and establish a corresponding mathematical model of the mixing silo system based on the actual equipment status and parameters to predict the system status of subsequent task cycles.

[0008] With the weight of raw materials in the silo as a constraint and the maximum amount of raw materials fed by the feeding trolley within the predicted time as the optimization objective, a performance index function is established.

[0009] Using the position-adding sequence as the decision variable and the performance index function as the optimization objective, an integer genetic algorithm is designed. For each population of the genetic algorithm, the maximum position-adding weight is calculated using the dual simplex method to obtain the optimized position-adding sequence.

[0010] The optimized feeding sequence is introduced into the mathematical model of the mixing silo system, and the original nonlinear mixed integer optimization model is transformed into a linear programming model. The dual simplex method is used to solve the linear programming model to obtain the start and end times of each feeding task.

[0011] By combining the optimized feeding sequence with the start and end times of each feeding task, the timing of automatic feeding is obtained.

[0012] In some optional implementations, the parameter information of the mixing silo system includes: the number of mixing silos l, and the upper limit W of the raw material weight in the mixing silos. max The lower limit W of the raw material weight in the silo min The distance d between adjacent silos, and the number of feeding trolleys n c The number of reclaimers corresponding to each feeding trolley is m. r The length of the conveyor belt between the silo and the corresponding material pile.

[0013] In some optional implementations, the status information of the mixing silo system includes: the type of raw material in the silo, the weight W of the raw material in the silo, and the discharge flow rate f of the silo. - The moving speed v of the feeding trolley c The feeding flow rate f of the feeding trolley + The moving speed v of the reclaimer r The location of the feeding trolley, p c The location p of the material handling machine r .

[0014] In some optional implementations, parameter and status information of the mixing silo system is obtained, and a corresponding mathematical model of the mixing silo system is established based on the actual equipment status and parameters, including:

[0015] Let x(i,j) represent whether the j-th hopper of the i-th task is filled with material, where 0 indicates no filling and 1 indicates filling. s (i) represents the start time of the feeding of the i-th task, te (i) represents the end time of feeding the i-th task, and the initial weight of the j-th hopper is W(0,j);

[0016] By W ts (k,j)=W te (k-1,j)-f - ·(t s (k)-t e (k-1)) Obtain the starting time of the feeding for the k-th task, and the weight W of the raw material in the j-th hopper. ts (k,j), where W te (k-1,j) represents the end time of the (k-1)th task's feeding, and f represents the weight of the raw materials in the j-th hopper. - ·(t s (k)-t e (k-1)) represents the end time t of the (k-1)th task feeding. e (k-1) and the start time t of the kth task feeding s (k) The amount of material discharged from the silo during this period;

[0017] By W te (k,j)=W ts (k,j)-f - ·(t e (k)-t s (k))+f + ·(t e (k)-t s (k))·x(k,j) yields the end time of the feeding for the k-th task, and the weight W of the raw material in the j-th hopper. te (k,j), where f - ·(t e (k)-t s (k) represents the end time t of the k-th task's feeding. e (k) and the start time t of the kth task feeding s (k) The amount of material discharged from the silo during this period, f + ·(t e (k)-t s (k) represents the end time t of the k-th task's feeding. e (k) and the start time t of the kth task feeding s (k) The amount of material added to the silo during this period, where x(k,j) indicates whether the j-th silo is added for the k-th task, 0 indicates no material is added, and 1 indicates material is added;

[0018] Depend on Get t k At time p, the position of the i-th feeding cart is... c (i,tk ), where p c (i,0) represents the initial position of the i-th feeding cart, v c This indicates the speed at which the feeding trolley moves;

[0019] Depend on Get t k At time p, the position of the i-th material handling machine r (i,t k ), where p r (i,0) represents the initial position of the i-th material handling machine, v r This indicates the moving speed of the material handling machine;

[0020] The time required to transport raw materials from the stockpile to the silo is calculated based on the type of material in each silo, the location of the corresponding stockpile for each type, and the belt speed.

[0021] In some alternative implementations, the constraints are: n represents the predicted number of task cycles, and l represents the number of mixing bins.

[0022] In some optional implementations, the performance metric function is: Among them, f + (k) represents the feeding flow rate of the feeding trolley for the kth task.

[0023] In some alternative implementations, the method further includes:

[0024] Monitor system status changes. If a change occurs that affects the silo sequence, recalculate the silo sequence for feeding and the start and end times of the feeding task. If no change occurs that affects the silo sequence, use the current feeding sequence until the entire mixing and stacking process is completed.

[0025] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0026] By establishing a mathematical model for mixing and batching, and predicting the system state for multiple subsequent task cycles, an automatic feeding sequence optimization problem is established, with the weight of raw materials in the silo as a constraint and optimal feeding efficiency as the performance index. The automatic feeding sequence is then programmed by designing a genetic algorithm and the dual simplex method to solve for the silo feeding sequence and the start and end times of each feeding task. This solves the problem of automatic silo feeding sequence programming in the raw material mixing and batching process, avoids the problem of empty silos affecting the quality of the mixed material, and improves system operating efficiency. Attached Figure Description

[0027] Figure 1This is an implementation flowchart of an optimized timing method for automatic feeding of mixing and batching bins provided in an embodiment of the present invention;

[0028] Figure 2 This is a graph showing the change in the weight of raw materials in each silo, provided in an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0030] This embodiment discloses a timing method for automatically feeding materials into a mixing and batching silo in a steel company, based on an optimized method. Manual scheduling relies on operational experience, which can easily lead to empty silos, affecting mixing quality and efficiency, and making it difficult to control production energy consumption. How to rationally determine the timing of automatic feeding into the mixing and batching silo to ensure no empty silos occur and to maximize the efficiency of the mixing process has become a pressing problem for the company.

[0031] like Figure 1 As shown, the timing sequence planning method for automatic feeding of the mixing silo based on optimization provided in this embodiment of the invention includes the following steps:

[0032] S001. Obtain system parameter information;

[0033] In this embodiment of the invention, the mixing tank includes 11 mixing bins, denoted as #1, #2...#11. The upper limit W of the raw material weight in each bin is... max =[736,736,736,736,736,736,736,736,520,520,520]t, the lower limit W of the raw material weight in the silo. min =50t, the distance between the centers of adjacent silos d = 10m, the number of feeding trolleys n c =1. The number of material handling machines (m) corresponding to each feeding trolley r =2.

[0034] S002. Read the system status information, which includes: the type of raw material in the hopper, denoted by a vector C, and the weight of the raw material in the hopper, W = 0.8 * W. max The material discharge flow rate f of the silo - = [100,150,100,150,150,50,50,100,50,50,50]t / h, where v is the moving speed of the feeding trolley. c=0.5m / s, the feeding flow rate f of the feeding trolley + =2000t / h, the moving speed v of the reclaimer r =0.6m / s, the position p of the feeding trolley c = [114, 25]m, the position p of the material handling machine r = [51,121]m.

[0035] S003. Let x(i,j) represent whether the j-th hopper of the i-th task is filled with material, where 0 indicates no filling and 1 indicates filling. s (i) represents the start time of the feeding of the i-th task, t e (i) represents the end time of the feeding of the i-th task, and the initial weight of the j-th hopper is W(0,j). The mathematical model of the mixing hopper is established as follows:

[0036] Step 3.1) At the start time of the material feeding for the k-th task, the weight of the raw material in the j-th hopper is:

[0037] W ts (k,j)=W te (k-1,j)-f - ·(t s (k)-t e (k-1)) (1)

[0038] Step 3.2) Following the formula in Step 3.1), we recursively derive:

[0039]

[0040] Step 3.3) At the end of the feeding process for the k-th task, the weight of the raw material in the j-th hopper is:

[0041] W te (k,j)=W ts (k,j)-f - (t e (k)-t s (k))+f + (t e (k)-t s (k))·x(k,j) (3)

[0042] Step 3.4) Based on the formula in Step 3.3), we get:

[0043]

[0044] Step 3.5)t k At time i, the position of the i-th feeding cart:

[0045]

[0046] Step 3.6)t k At time i, the position of the i-th material handling machine:

[0047]

[0048] Step 3.7) Calculate the time required to transport raw materials from the stockpile to the silo based on the type of material in each silo, the location of the corresponding stockpile for each type, and the belt speed.

[0049] S004. Set Constraints: To prevent the silo from becoming empty during the mixing process, set the following constraints:

[0050]

[0051] S005. Setting the performance index function: With the optimization objective of maximizing the amount of raw material added by the hopper cart within the prediction time, the performance index function is established as follows:

[0052]

[0053] Where f + (k) represents the feeding flow rate of the feeding trolley in the kth bin, and the prediction period n is set to 36.

[0054] S006. Using the addition sequence x(k,j) as the decision variable and the performance index established in step S005 as the optimization objective, design an integer genetic algorithm. For each genetic algorithm population, calculate the maximum addition weight using the dual simplex method to obtain the optimized addition sequence.

[0055] S007. Substitute the feeding sequence obtained in step S006 into the mathematical model of the mixing silo established in step S003, transforming the original nonlinear mixed-integer optimization model into a linear programming model. Use the dual simplex method to solve this linear programming model to obtain the start time t of each feeding task. s (k) and end time t e (k);

[0056] S008. By combining the loading sequence calculated in step S006 and the start and end times of loading calculated in step S007, the timing of automatic material supply can be obtained;

[0057] S009. Monitor system status changes. If any changes occur affecting the silo sequence, such as silo type or feed rate, proceed to step S006 to recalculate the silo sequence for feeding and the start and end times of the feeding task. If no such changes occur, use the feeding sequence until the entire mixing and stacking process is completed. Figure 2The graph shown reflects the changes in the weight of raw materials in silos #1, #2... #11.

[0058] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0059] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A timing-based method for automatic feeding of a mixing and batching silo based on optimization, characterized in that, include: Obtain parameter and status information of the mixing silo system, and establish a corresponding mathematical model of the mixing silo system based on the actual equipment status and parameters to predict the system status of subsequent task cycles. With the weight of raw materials in the silo as a constraint and the maximum amount of raw materials fed by the feeding trolley within the predicted time as the optimization objective, a performance index function is established. Using the position-adding sequence as the decision variable and the performance index function as the optimization objective, an integer genetic algorithm is designed. For each population of the genetic algorithm, the maximum position-adding weight is calculated using the dual simplex method to obtain the optimized position-adding sequence. The optimized feeding sequence is introduced into the mathematical model of the mixing silo system, and the original nonlinear mixed integer optimization model is transformed into a linear programming model. The dual simplex method is used to solve the linear programming model to obtain the start and end times of each feeding task. By combining the optimized feeding sequence with the start and end times of each feeding task, the timing of automatic feeding is obtained.

2. The method according to claim 1, characterized in that, The parameters of the mixing silo system include: the number of mixing silos l, and the upper limit W of the raw material weight in the mixing silos. max The lower limit W of the raw material weight in the silo min The distance d between adjacent silos, and the number of feeding trolleys n c The number of reclaimers corresponding to each feeding trolley is m. r The length of the conveyor belt between the silo and the corresponding material pile.

3. The method according to claim 2, characterized in that, The status information of the mixing silo system includes: the type of raw material in the silo, the weight W of the raw material in the silo, and the discharge flow rate f of the silo. - The moving speed v of the feeding trolley c The feeding flow rate f of the feeding trolley + The moving speed v of the reclaimer r The location of the feeding trolley, p c The location p of the material handling machine r .

4. The method according to claim 3, characterized in that, Obtain the parameter and status information of the mixing silo system, and establish a corresponding mathematical model of the mixing silo system based on the actual equipment status and parameters, including: Let x(i,j) represent whether the j-th hopper of the i-th task is filled with material, where 0 indicates no filling and 1 indicates filling. s (i) represents the start time of the feeding of the i-th task, t e (i) represents the end time of feeding the i-th task, and the initial weight of the j-th hopper is W(0,j); By W ts (k,j)=W te (k-1,j)-f - ·(t s (k)-t e (k-1)) Obtain the starting time of the feeding for the k-th task, and the weight W of the raw material in the j-th hopper. ts (k,j), where W te (k-1,j) represents the end time of the (k-1)th task's feeding, and f represents the weight of the raw materials in the j-th hopper. - ·(t s (k)-t e (k-1)) represents the end time t of the (k-1)th task feeding. e (k-1) and the start time t of the kth task feeding s (k) The amount of material discharged from the silo during this period; By W te (k,j)=W ts (k,j)-f - ·(t e (k)-t s (k))+f + ·(t e (k)-t s (k))·x(k,j) yields the end time of the feeding for the k-th task, and the weight W of the raw material in the j-th hopper. te (k,j), where f - ·(t e (k)-t s (k) represents the end time t of the k-th task's feeding. e (k) and the start time t of the kth task feeding s (k) The amount of material discharged from the silo during this period, f + ·(t e (k)-t s (k) represents the end time t of the k-th task's feeding. e (k) and the start time t of the kth task feeding s (k) The amount of material added to the silo during this period, where x(k,j) indicates whether the j-th silo is added for the k-th task, 0 indicates no material is added, and 1 indicates material is added; Depend on Get t k At time p, the position of the i-th feeding cart is... c (i,t k ), where p c (i,0) represents the initial position of the i-th feeding cart, v c This indicates the speed at which the feeding trolley moves; Depend on Get t k At time p, the position of the i-th material handling machine r (i,t k ), where p r (i,0) represents the initial position of the i-th material handling machine, v r This indicates the moving speed of the material handling machine; The time required to transport raw materials from the stockpile to the silo is calculated based on the type of material in each silo, the location of the corresponding stockpile for each type, and the belt speed.

5. The method according to claim 4, characterized in that, The constraints are as follows: n represents the predicted number of task cycles, and l represents the number of mixing bins.

6. The method according to claim 5, characterized in that, The performance index function is: Among them, f + (k) represents the feeding flow rate of the feeding trolley for the kth task.

7. The method according to claim 1, characterized in that, The method further includes: Monitor system status changes. If a change occurs that affects the silo sequence, recalculate the silo sequence for feeding and the start and end times of the feeding task. If no change occurs that affects the silo sequence, use the current feeding sequence until the entire mixing and stacking process is completed.

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