Method and device for scheduling optimization of electrode graphitization process and storage medium

Abstract

The invention relates to an electrode graphitization process scheduling optimization method and device and a storage medium, scheduling characteristics of each process in an electrode graphitization process are obtained, the processes comprise a charging process, an electrifying process, a cooling process and a discharging process, and the scheduling characteristics comprise time-of-use electricity price information; setting a related decision variable of the power utilization state, determining a constraint condition according to the scheduling characteristic of each process and the related decision variable of the power utilization state, determining a scheduling objective function taking the minimization of the total electric charge cost as an objective, and constructing a scheduling optimization model according to the constraint condition and the scheduling objective function; and obtaining scheduling and production scheduling parameters, solving the scheduling optimization model by using the scheduling and production scheduling parameters, and determining a target scheduling scheme. Therefore, while the maximum allowable completion time is considered, the goal of minimizing the electric charge cost is achieved, and the method has remarkable economic benefits and practical values.

Description

Technical Field

[0001] The present application mainly relates to the field of electrode graphitization process, and in particular to a method, device and storage medium for scheduling optimization of electrode graphitization process. Background Art

[0002] Graphite electrodes, as high-temperature-resistant conductive materials, are widely used in metallurgy, chemical engineering, and power generation. Their production is energy-intensive, with the graphitization process being both a key step in graphite electrode production and the primary electricity consumer, accounting for approximately 70% of total electricity consumption. Therefore, there is significant potential for energy cost savings by rationally scheduling manufacturing tasks. Scheduling the electrode graphitization process is central to production management in refineries and plays a key role in reducing costs, improving quality, and increasing efficiency. Production scheduling is currently primarily based on time-of-use (TOU) electricity pricing, a widely adopted load control strategy in global energy markets. This policy stems from the significant daily fluctuations in total grid electricity demand. Consequently, power suppliers often employ TOU pricing to effectively balance supply and demand. TOU pricing typically divides the day into peak and off-peak periods. In some TOU pricing schemes implemented in my country, peak-period electricity prices can be as much as three times higher than off-peak prices. This policy can encourage users to optimize their electricity usage behavior and achieve a dynamic balance between supply and demand. In particular, it can encourage manufacturing companies with high electricity usage to strategically coordinate production processes and control electricity costs through production timing optimization, which has a significant economic driving effect.

[0003] Although the current dispatching scheme responds to the time-of-use electricity price policy, it still uses traditional spreadsheets when formulating the scheme, which has three shortcomings: (1) It relies on human experience and is highly subjective, making it difficult to ensure the quality of the dispatching scheme; (2) It lacks a systematic modeling method and cannot effectively integrate complex constraints; (3) It has a weak ability to respond to dynamic demand and low adjustment efficiency. Summary of the Invention

[0004] One purpose of the present application is to provide a method, device and storage medium for scheduling optimization of an electrode graphitization process, so as to solve the problems of low solution efficiency, great optimization difficulty and poor versatility of optimization solutions in the prior art.

[0005] According to one aspect of the present application, a method for optimizing scheduling of an electrode graphitization process is provided, the method comprising:

[0006] Obtaining the scheduling characteristics of each process in the electrode graphitization process, wherein the processes include the furnace loading process, the power-on process, the cooling process, and the furnace unloading process, and the scheduling characteristics include time-of-use electricity price information;

[0007] Set the relevant decision variables of the power consumption status, determine the constraints based on the scheduling characteristics of each process and the relevant decision variables of the power consumption status, and determine the scheduling objective function with the goal of minimizing the total electricity cost. Build a scheduling optimization model based on the constraints and scheduling objective function;

[0008] Obtain scheduling parameters, use the scheduling parameters to solve the scheduling optimization model, and determine the target scheduling plan.

[0009] Optionally, the scheduling characteristics further include any one or any combination of the following:

[0010] The type and quantity of electrodes processed by each furnace group;

[0011] Each process and corresponding time information when processing electrodes in each sub-furnace in each furnace group;

[0012] The power supply information of the transformer used by the neutron furnace of each furnace group and the maximum total power threshold of the total transformer used by all furnace groups;

[0013] The power curve corresponding to each electrode type when the sub-furnace is powered on;

[0014] Time-of-use electricity price information used by all sub-furnaces during the power-on process.

[0015] Optionally, the constraints include any one or any combination of power-on process constraints, mid-power-off operation constraints, related variable constraints in the power-on process, process constraints in electrode batches, electrode type and sub-furnace selection constraints for each electrode batch, and electrode power constraints for each electrode batch in the power-on process.

[0016] Optionally, the constraint condition further includes an initial state constraint condition, and the method includes:

[0017] The initial state constraint conditions are determined according to the time information of the power-on process, the time information of the furnace loading process, the time information of the cooling process and the time information of the furnace unloading process in the electrode batch.

[0018] Optionally, the power usage status-related decision variables include power-on-related decision variables of the furnace group in the electrode batch, and the constraint conditions are determined according to the scheduling characteristics of each process and the power usage status-related decision variables, including:

[0019] According to the time information of the power-on process of the furnace group in the electrode batch and the power-on related decision variables of the furnace group in the electrode batch, the power-on process constraints, the midway power-off operation constraints and the related variable constraints in the power-on process are determined.

[0020] Optionally, the power usage status-related decision variables include power-on-related decision variables of the furnace group in the electrode batch, and the constraint conditions are determined according to the scheduling characteristics of each process and the power usage status-related decision variables, including:

[0021] The process constraints in the electrode batch are determined based on the electrode type and the time information of the designated process in the furnace group, the time information of the power-on process of the furnace group in the electrode batch, and the power-on-related decision variables of the furnace group in the electrode batch, wherein the designated process time information includes the start time, end time and duration of the process, and the designated process includes the furnace loading process, the cooling process and the furnace unloading process.

[0022] Optionally, the electrode type and sub-furnace selection constraints for each electrode batch include:

[0023] The constraint that each electrode batch can only process one electrode type;

[0024] The constraint that each electrode batch can only work in one sub-furnace;

[0025] The constraint that adjacent electrode batches cannot work in the same sub-furnace.

[0026] Optionally, the power usage state-related decision variables include electrode power decision variables, and the constraint conditions are determined according to the scheduling characteristics of each process and the power usage state-related decision variables, including:

[0027] The electrode power constraints of each electrode batch in the power-on process are determined based on the power supply information of the transformer used by the sub-furnaces in each furnace group, the power curve of the corresponding processing electrode type when each sub-furnace is powered on, and the electrode power decision variables.

[0028] Optionally, the constraint condition further includes:

[0029] The processing volume of each electrode must meet the total demand;

[0030] The power consumption of all furnace groups at the same time cannot exceed the maximum total power threshold;

[0031] All electrode batches must be processed before the maximum permissible completion time.

[0032] Optionally, obtaining scheduling parameters and solving the scheduling optimization model using the scheduling parameters includes:

[0033] Obtain scheduling parameters, import the scheduling parameters into the scheduling optimization model, and use the CPLEX solver to perform optimization and solution, wherein the scheduling parameters include any one or any combination of scheduling time period, time-of-use electricity price information, furnace group parameters, time requirements of each process, and production characteristics of the power-on process.

[0034] According to another aspect of the present application, a device for optimizing the scheduling of an electrode graphitization process is provided, the device comprising:

[0035] one or more processors; and

[0036] A memory storing computer-readable instructions, which, when executed, cause the processor to perform the operations of the method described above.

[0037] According to another aspect of the present application, a computer-readable medium is provided, on which computer instructions are stored. The computer-readable instructions can be executed by a processor to implement the method described above.

[0038] Compared with the prior art, the present application obtains the scheduling characteristics of each process in the electrode graphitization process, wherein the processes include the furnace loading process, the power-on process, the cooling process, and the furnace unloading process, and the scheduling characteristics include time-of-use electricity price information; sets relevant decision variables for the power consumption status, determines the constraints according to the scheduling characteristics of each process and the relevant decision variables for the power consumption status, and determines the scheduling objective function with the goal of minimizing the total electricity cost, and constructs a scheduling optimization model based on the constraints and the scheduling objective function; obtains scheduling production parameters, uses the scheduling production parameters to solve the scheduling optimization model, and determines the target scheduling scheme. This not only significantly improves the efficiency of solving the scheduling problem of the electrode graphitization process, but also improves the production efficiency of the workshop. While considering the maximum allowable completion time, it achieves the goal of minimizing the electricity cost. It has significant economic benefits and practical value and can provide efficient scheduling solutions for related enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to make the above-mentioned objects, features and advantages of the present application more clearly understood, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings, wherein:

[0040] Figure 1 A schematic flow chart of a method for optimizing scheduling of an electrode graphitization process according to one aspect of the present application is shown;

[0041] Figure 2 A schematic diagram illustrating a processing embodiment of the electrode graphitization process scheduling problem in one embodiment of the present application is shown;

[0042] Figure 3 A schematic diagram showing the solution results of 12 groups of examples in a specific embodiment of the present application is shown;

[0043] Figure 4 A schematic diagram showing a comparison of power costs between an optimized solution and a solution before optimization in a specific embodiment of the present application;

[0044] Figure 5 A schematic diagram showing a scheduling Gantt chart in a specific embodiment of the present application is shown;

[0045] Figure 6 A system block diagram of an apparatus for optimizing scheduling of an electrode graphitization process according to an embodiment of the present application is shown.

[0046] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objectives, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0049] As used in this application and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0050] Figure 1 A schematic flow chart of a method for optimizing scheduling of an electrode graphitization process according to one aspect of the present application is shown. The method includes steps S11 to S13.

[0051] Step S11, obtaining the scheduling characteristics of each process in the electrode graphitization process, wherein the processes include the furnace loading process, the power-on process, the cooling process and the furnace unloading process, and the scheduling characteristics include time-of-use electricity price information; here, the characteristics of the scheduling problem of the electrode graphitization process are analyzed, and the scheduling characteristics in each process are specifically analyzed. The processes involved in the electrode graphitization process include the furnace loading process, the power-on process, the cooling process and the furnace unloading process. Combined with the time-of-use electricity price information, the scheduling characteristics are used to determine complex constraints and scheduling goals.

[0052] In one embodiment of the present application, the scheduling characteristics include any one or any combination of the following: the type and quantity of electrodes processed by each furnace group; each process and corresponding time information when each sub-furnace in each furnace group processes electrodes; the power supply information of the transformer used by the sub-furnace in each furnace group and the maximum total power threshold of the total transformer used by all furnace groups; the power curve corresponding to each type of electrode when the sub-furnace is powered on; and the time-of-use electricity price information used by all sub-furnaces during the power-on process. In this context, under time-of-use electricity pricing, a given number of electrodes to be processed are processed in multiple cascaded furnace groups, each with different processing capabilities to handle different electrode types and quantities. Each furnace group consists of multiple sub-furnaces, which sequentially handle multiple processes for processing a given batch of electrodes, including loading, power-on, cooling, and unloading. Each process has its own time requirements. Furthermore, the transformer power supply information used means that each furnace group has an independent transformer, and at any given time, each transformer can only power one sub-furnace within the same furnace group. All furnace groups share a master transformer, with an upper limit on the total power. The power consumption of a sub-furnace when powered on is determined by the power curve of the electrode type being processed, which varies nonlinearly over the power-on time. Furthermore, due to time-of-use electricity pricing, electricity prices are divided into high and low periods within a day. If a sub-furnace encounters a high-price period during the power-on process, the company can temporarily shut down the power to avoid the high-price period, thereby reducing overall electricity costs. Due to the insulation of the sub-furnaces, the temperature inside the sub-furnace is largely unaffected after powering back on, without affecting the power curve of the electrode material.

[0053] Step S12, setting the relevant decision variables of the power consumption status, determining the constraints according to the scheduling characteristics of each process and the relevant decision variables of the power consumption status, and determining the scheduling objective function with the goal of minimizing the total electricity cost, and constructing a scheduling optimization model based on the constraints and the scheduling objective function; here, after analyzing the characteristics of the scheduling problem, the constraints and scheduling objectives are determined using the scheduling characteristics and the set relevant decision variables of the power consumption status, and then constructing a scheduling objective function with the scheduling objective of minimizing the total electricity cost used in production. The scheduling objective function solves the electrode resource allocation problem, the scheduling batch sorting problem and the production timing arrangement problem while considering the maximum allowable completion time, that is, allocating a processing furnace group and a sub-furnace to each electrode to be processed, determining the order of electrode processing and the start and end time of all processes in each electrode batch; the scheduling optimization model is constructed by the scheduling objective function and the corresponding constraints, wherein the relevant decision variables of the power consumption status include relevant variables such as power on, power off, power restoration, power transmission power and process, and 0-1 variables are used for decision making.

[0054] Step S13, obtain the scheduling parameters, use the scheduling parameters to solve the scheduling optimization model, and determine the target scheduling plan. Here, the scheduling parameters are set based on the scheduling optimization model, and the scheduling parameters are imported into the established scheduling optimization model. The solver is used for optimization to verify the practicality and effectiveness of the method. Through the integrated modeling and collaborative optimization of the above three sub-problems, the optimized scheduling plan that minimizes the total electricity cost is obtained, thereby improving the production efficiency of the electrode graphitization process. The optimization results can also be compared and analyzed with the manual scheduling plan before optimization. Figure 2 As shown, a schematic diagram of a processing embodiment of the electrode graphitization process scheduling problem is shown. Multiple electrode raw materials are processed by multiple furnace groups. Each electrode raw material is assigned to a corresponding furnace group for processing. A furnace group has multiple sub-furnaces. For example, furnace group 1 processes electrode raw material 3, electrode raw material 5 and electrode raw material 6, and furnace group F processes electrode raw material 1, electrode raw material 7 and electrode raw material 9. Each furnace group has multiple sub-furnaces, and each sub-furnace includes multiple processes, namely, furnace loading process, power supply process, interruption process, cooling process and furnace unloading process. After multiple processes, it is processed into the corresponding product.

[0055] In one embodiment of the present application, the constraints include any one or any combination of power-on process constraints, power-off operation constraints, related variable constraints in the power-on process, process constraints in electrode batches, electrode type and sub-furnace selection constraints for each electrode batch, and electrode power constraints for each electrode batch in the power-on process. Here, a discrete time representation method is used to define parameters and decision variables, convert the scheduling objectives and constraints of the electrode graphitization process scheduling problem into mathematical expressions, and construct a mixed integer linear programming model, i.e., a scheduling optimization model. Specifically, as follows:

[0056] In step S12, the constraints also include initial state constraints, which are determined based on the time information of the power-on process, the time information of the furnace loading process, the time information of the cooling process, and the time information of the furnace unloading process in the electrode batch. g,n , PPT g,n 、PIT g,n 、PRT g,n , LST g,n 、LPT g,n 、CST g,n 、CPT g,n OST g,n 、OPT g,n are all positive numbers, ensuring the non-negativity of the start and end times of all processes and power-off operations in the electrode batch of each furnace group; where G represents the set of production furnace groups, furnace group g∈G, electrode batch set N, electrode batch n∈N, PST g,nIndicates the start time of the energization process of furnace group g in electrode batch n, PPT g,n PIT represents the end time of the energization process of furnace group g in electrode batch n. g,n Indicates the time when the power is cut off during the power-on process of furnace group g in electrode batch n, PRT g,n Indicates the end time of power failure of furnace group g in electrode batch n, LST g,n Indicates the start time of the furnace loading process of furnace group g in electrode batch n, LPT g,n represents the end time of the furnace loading process of furnace group g in electrode batch n, CST g,n represents the start time of the cooling process of furnace group g in electrode batch n, CPT g,n OST represents the end time of the cooling process of furnace group g in electrode batch n. g,n Indicates the start time of the unloading process of furnace group g in electrode batch n, OPT g,n Indicates the time when the unloading process of furnace group g in electrode batch n is completed.

[0057] In step S12, the power-use state-related decision variables include the power-on-related decision variables of the furnace group in the electrode batch. Based on the time information of the power-on process of the furnace group in the electrode batch and the power-on-related decision variables of the furnace group in the electrode batch, the power-on process constraints, the mid-way power-off operation constraints, and the power-on process-related variable constraints are determined. Here, the power-on process constraints satisfy the following formula:

[0058]

[0059]

[0060]

[0061]

[0062] i∈I, I represents the set of electrode types, electrode type i∈I; T represents the set of time points after the scheduling time is discretized, time point t∈T; TL represents the length of the discrete time interval, PS g,n,t is a decision variable. If, at time slot t, furnace group g starts to be energized at electrode batch n, then PS g,n,t =1, otherwise PS g,n,t =0;PP g,n,t is a decision variable. If, at time slot t, furnace group g is energized at the end of electrode batch n, then PP g,n,t =1, otherwise PP g,n,t=0; the above formulas (1)-(2) respectively define the start and end time of the power-on process of each electrode batch when the furnace group is working; formulas (3)-(4) ensure the uniqueness of the power-on process in each electrode batch. It should be noted that the power-on-related decision variables of the furnace group in the electrode batch include PS g,n,t and PP g,n,t .

[0063] The power outage operation constraint condition satisfies the following formula:

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] PI g,n,t represents the decision variable. If at time slot t, the furnace group g loses power in the middle of electrode batch n, then PI g,n,t =1, otherwise PI g,n,t =0;PR g,n,t represents the decision variable. If, at time slot t, furnace group g is powered on in the middle of electrode batch n, then PR g,n,t =1, otherwise PR g,n,t =0; TImax represents the maximum duration of a short interruption operation in the power-on process. Formulas (5)-(6) respectively define the time for power-off and power-on of each electrode batch during the power-on process; Formulas (7)-(8) ensure that power-off and power-on operations can occur at most once; Formula (9) limits the duration of power-off; Formulas (10)-(11) ensure that power-off operations can only occur during the power-on process. It should be noted that the power-on-related decision variables of the furnace group in the electrode batch include PI g,n,t and PR g,n,t .

[0072] The relevant variable constraints in the power-on process satisfy the following formula:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] TNmax represents the maximum duration of the switching process between adjacent electrode batches, P g,t represents the decision variable. If at time slot t, the furnace group g is supplying power and is not in a power-off state, then P g,t =1, otherwise P g,t =0;PN g,n,t Represents the decision variable. If at time slot t, electrode batch n in furnace group g is in the power-on process, then PN g,n,t =1, otherwise PN g,n,t =0; Formula (12) defines the time requirement for switching between the power-on processes of adjacent electrode batches; Formulas (13)-(14) calculate and determine the short power-off period of the electrode batch in the power-on process; Formula (15) ensures that at most only one electrode batch in a single furnace group is in the power-on process at the same time; Formula (16) ensures the transition of the power-on starting state of each electrode batch.

[0079] In step S12, the power usage state-related decision variables include the power-on-related decision variables for the furnace group within the electrode batch. The process constraints within the electrode batch are determined based on the electrode type and the designated process time information within the furnace group, the time information for the power-on process of the furnace group within the electrode batch, and the power-on-related decision variables for the furnace group within the electrode batch. The designated process time information includes the process start time, end time, and duration. The designated processes include the furnace loading process, the cooling process, and the furnace unloading process. The process constraints within the electrode batch include the time constraints for each process within the electrode batch and the order constraints for each process within the electrode batch. The time constraints for each process within the electrode batch satisfy the following formula:

[0080]

[0081]

[0082]

[0083]

[0084] Powertime i,g Indicates the duration of power on of electrode type i in furnace group g; Loadtime i,g Cooltime represents the loading time of electrode type i in furnace group g; i,g Indicates the cooling time of electrode type i in furnace group g; Outtimei,g PNI represents the time it takes for electrode type i to be removed from furnace group g; g,n,i Denotes the decision variable. If furnace group g processes electrode type i in electrode batch n, then PNI g,n,i =1, otherwise PNI g,n,i =0; It should be noted that the decision variables related to the power-on of the furnace group in the electrode batch include PNI g,n,i .

[0085] The order constraints of the processes in each electrode batch satisfy the following formula:

[0086]

[0087]

[0088]

[0089] The above formula (21) constrains the order of the furnace loading process to be earlier than the power-on process, formula (22) constrains the order of the power-on process to be earlier than the cooling process, and formula (23) constrains the order of the cooling process to be earlier than the furnace unloading process.

[0090] In step S12, the electrode type and sub-furnace selection constraints for each electrode batch include: each electrode batch can only process one electrode type; each electrode batch can only work in one sub-furnace; and adjacent electrode batches cannot work in the same sub-furnace. Here, the electrode type and sub-furnace selection constraints for each electrode batch satisfy the following formula:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] In the above formula, V represents the sub-furnace set, sub-furnace v∈V, PV g,n,v represents the decision variable. If the sub-furnace v of the furnace group g is processing electrode batch n, then PV g,n,v =1, otherwise PV g,n,v =0; Formulas (24)-(25) restrict each electrode batch to process only one electrode type; Formulas (26)-(27) constrain each electrode batch to work in only one sub-furnace; Formula (28) constrains adjacent electrode batches from working in the same sub-furnace.

[0097] In step S12, the power consumption state-related decision variables include electrode power decision variables. The electrode power constraints for each electrode batch during the power-on process are determined based on the transformer power supply information used by each sub-furnace in each furnace group, the power curve of the processing electrode type corresponding to each sub-furnace when powered on, and the decision variables. Here, the relevant electrode power constraints satisfy the following formula:

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] In the above formula, TI represents the time point set of the electrode power curve, and at a certain time point ti∈TI, XTI g,t,ti is a decision variable. If at time slot t, the furnace group g is supplying power, and the timing of the power supply is ti, then XTI g,t,ti =1, otherwise XTI g,t,ti =0; Formula (29) ensures that each furnace group can only process one power-on process at a time; Formula (30) limits the power-on time of each electrode type; Formulas (31)-(33) ensure that if a power-off operation is performed in the middle of the power-on process of each electrode batch, the power curve will be postponed with the interruption process to facilitate subsequent electricity fee calculation; It should be noted that the electrode power decision variables include XTI g,t,ti .

[0104] In step S12, the constraints also include: the processing volume of each type of electrode must meet the total demand; the power consumption of all furnace groups at the same time cannot exceed the maximum total power threshold; and all electrode batches must be processed before the maximum allowable completion time. Here, the constraints also include other related constraints, among which the processing volume of each type of electrode must meet the total demand and satisfy the following formula:

[0105]

[0106] Among them, Dem i Indicates the processing requirements of electrode type i.

[0107] The power consumption of all furnace groups at the same time cannot exceed the total power limit and must meet the following formula:

[0108]

[0109] Among them, Power U Indicates the upper limit of the total power consumption of all furnace groups, Power i,ti Indicates the power consumption of electrode type i at time ti of the power curve.

[0110] All electrode batches must be processed before the maximum allowable completion time:

[0111]

[0112] In the above formula, TC max Indicates the maximum allowable completion time of all electrode batches.

[0113] Therefore, the objective function is to minimize the total electricity cost:

[0114]

[0115] Among them, TEC represents the total electricity cost, Price t represents the electricity price at time t.

[0116] In a specific embodiment of the present application, the relevant constraints are constructed by the above formulas (1)-(36), the objective function is constructed by formula (37), and then the scheduling optimization model is obtained according to the constraints and the objective function. The method described in the present application is to minimize the electricity cost as the goal, while considering the requirement of the maximum allowable completion time, to solve the electrode resource allocation problem, the scheduling batch sorting problem and the production time sequence arrangement problem, that is, to allocate a processing furnace group and a sub-furnace for each electrode to be processed, determine the order of the processing batch and the start and end time of each process, and find an optimization solution for the scheduling problem of the electrode graphitization process through integrated modeling and collaborative optimization of the three sub-problems, so as to provide enterprises with scheduling decision support with both theoretical optimality and engineering practicality.

[0117] In some embodiments of the present application, in step S13, scheduling parameters are obtained, imported into the scheduling optimization model, and optimized using the CPLEX solver. The scheduling parameters include any one or any combination of the scheduling time period, time-of-use electricity price information, furnace group parameters, time requirements for each process, and production characteristics of the power-on process. Here, the scheduling time period, time-of-use electricity price information, furnace group parameters, time requirements for each process, and production characteristics of the power-on process are set. The furnace group parameters include quantity, charging type range, and charging capacity, and the production characteristics of the power-on process include electrode power curve and total power upper limit.

[0118] Some embodiments of this application are generated based on actual cases of electrode graphitization process considering time-of-use electricity price. In a specific embodiment of this application, the discrete time interval of the scheduling cycle is set to 4 hours, the maximum number of furnace groups is 4 groups, the maximum number of sub-furnaces is 4, the maximum number of electrode batches per furnace group is 10, and the total number of electrode raw materials to be produced is 2. Among them, the data information of obtaining time-of-use electricity price is shown in Table 1:

[0119] time Electricity price / yuan 0:00-8:00 0.45 8:00-12:00 0.61 12:00-16:00 0.32 16:00-20:00 0.61 20:00-24:00 0.45

[0120] Table 1

[0121] The data information of the furnace group and electrode process is shown in Table 2:

[0122]

[0123] Table 2

[0124] The data information of the power curve is shown in Table 3:

[0125]

[0126]

[0127] Table 3

[0128] The data information obtained above is imported into the established scheduling optimization model, and the CPLEX solver is used for optimization to verify the practicality and effectiveness of the method, and output the optimal solution scheduling scheme with the lowest electricity cost. In order to verify the effectiveness of the model, a total of 12 groups of examples are solved in a specific embodiment of this application, and the solution result information is as follows: Figure 3 As shown, the test results of 12 real cases are given, and multiple combinations of furnace groups, sub-furnaces and electrode batches are selected respectively. The number of discrete variables, continuous variables and constraint equations in each set of implementation cases is given. The optimized electricity fee represents the optimal target solution that can be obtained by this method within 1000s, Gap represents the optimal solution deviation, and time is the solution time. When the Gap value is set to less than 0.01, it means that the model solves and finds the optimal solution, and the solution time is less than 1000s; if the Gap value is not 0, it means that the model has not found or proved that the solution is the optimal solution, and the upper limit of the solution time is set to 1000s. From Figure 2 The results show that as the problem scale increases, the problem solution space becomes larger, the number of decision variables, continuous variables and constraint equations increases, branch and bound becomes difficult, and the solution time increases.

[0129] Compare the electricity cost before optimization with the electricity cost using the above optimization solution. The comparison results are as follows: Figure 4As shown, it can be seen that the optimized electricity cost of each embodiment is lower than the electricity cost when waiting without power outage. After optimization using the scheduling optimization model of this application, the electricity cost can be reduced by more than 15%.

[0130] In one embodiment of the present application, a scheduling Gantt chart can be generated based on the solution of the scheduling optimization model, wherein the scheduling Gantt chart includes furnace group information, electrode batch information, and process information included in each electrode batch. Figure 5 As shown, based on Figure 4 The scheduling Gantt chart is made based on the solution results of the 12th embodiment in the , where the four sub-graphs represent 4 furnace groups, each furnace group contains 4 sub-furnaces, each furnace group produces 10 electrode batches, and each electrode batch contains multiple processes, A represents the furnace loading process, B represents the power-on process, C represents the mid-way power-off process, D represents the cooling process, and E represents the furnace unloading process. At most one mid-way power-off operation can be taken during the power-on process.

[0131] Figure 6 The system block diagram of the device for optimizing the scheduling of the electrode graphitization process according to one embodiment of the present application is shown. Figure 6 As shown, the device 600 for scheduling and optimizing the electrode graphitization process may include an internal communication bus 601, a processor 602, a read-only memory (ROM) 603, a random access memory (RAM) 604, and a communication port 605. When applied on a personal computer, the device 600 for scheduling and optimizing the electrode graphitization process may also include a hard disk 606. The internal communication bus 601 can realize data communication between the components of the device 600 for scheduling and optimizing the electrode graphitization process. The processor 602 can make judgments and issue prompts. In some embodiments, the processor 602 can be composed of one or more processors. The communication port 605 can realize data communication between the device 600 for scheduling and optimizing the electrode graphitization process and the outside. In some embodiments, the device 600 for scheduling and optimizing the electrode graphitization process can send and receive information and data from the network through the communication port 605. The apparatus 600 for optimizing the scheduling of the electrode graphitization process may also include various forms of program storage units and data storage units, such as a hard disk 606, a read-only memory (ROM) 603, and a random access memory (RAM) 604, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 602. The processor executes these instructions to implement the main part of the method. The results of the processor processing are transmitted to the user device via a communication port and displayed on the user interface.

[0132] The above-mentioned operating method can be implemented as a computer program, stored in the hard disk 606, and loaded into the processor 602 for execution to implement the method for optimizing the scheduling of the electrode graphitization process of the present application.

[0133] The present application also provides a computer-readable medium having computer instructions stored thereon, and the computer-readable instructions can be executed by a processor to implement the method for optimizing scheduling of an electrode graphitization process as described above.

[0134] When the method for optimizing the scheduling of the electrode graphitization process is implemented as a computer program, it can also be stored in a computer-readable storage medium as an article of manufacture. For example, a computer-readable storage medium may include, but is not limited to, a magnetic storage device (e.g., a hard disk, a floppy disk, a magnetic strip), an optical disk (e.g., a compact disk (CD), a digital versatile disk (DVD)), a smart card, and a flash memory device (e.g., an electrically erasable programmable read-only memory (EPROM), a card, a stick, a key drive). In addition, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" may include, but is not limited to, wireless channels and various other media (and / or storage media) that can store, contain, and / or carry code and / or instructions and / or data.

[0135] It should be understood that the embodiments described above are merely illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or a combination thereof.

[0136] Some aspects of the present application can be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software can be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor can be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors or combinations thereof. In addition, various aspects of the present application may be expressed as computer products located in one or more computer-readable media, which include computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs...), smart cards, and flash memory devices (e.g., cards, sticks, key drives...).

[0137] A computer-readable medium may include a propagated data signal embodying computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination thereof. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transmit the program for use. The program code on the computer-readable medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or any combination of the above.

[0138] The basic concepts have been described above. It will be apparent to those skilled in the art that the above disclosures are merely illustrative and do not constitute limitations on this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to this application. Such modifications, improvements, and amendments are suggested in this application and remain within the spirit and scope of the exemplary embodiments of this application.

[0139] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0140] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values are as accurate as possible within the feasible range.

Claims

1. A method for optimizing the scheduling of an electrode graphitization process, characterized in that: The method comprises: Obtaining the scheduling characteristics of each process in the electrode graphitization process, wherein the processes include the furnace loading process, the power-on process, the cooling process, and the furnace unloading process, and the scheduling characteristics include time-of-use electricity price information; Set the relevant decision variables of the power consumption status, determine the constraints based on the scheduling characteristics of each process and the relevant decision variables of the power consumption status, and determine the scheduling objective function with the goal of minimizing the total electricity cost. Build a scheduling optimization model based on the constraints and scheduling objective function; Obtain scheduling parameters, use the scheduling parameters to solve the scheduling optimization model, and determine the target scheduling plan.

2. The method according to claim 1, characterized in that The scheduling characteristics also include any one or any combination of the following: The type and quantity of electrodes processed by each furnace group; Each process and corresponding time information when processing electrodes in each sub-furnace in each furnace group; The power supply information of the transformer used by the neutron furnace of each furnace group and the maximum total power threshold of the total transformer used by all furnace groups; The power curve corresponding to each electrode type when the sub-furnace is powered on; Time-of-use electricity price information used by all sub-furnaces during the power-on process.

3. The method according to claim 1, characterized in that The constraints include any one or any combination of power-on process constraints, mid-power-off operation constraints, related variable constraints in the power-on process, process constraints in electrode batches, electrode type and sub-furnace selection constraints for each electrode batch, and electrode power constraints for each electrode batch in the power-on process.

4. The method according to claim 3, characterized in that The constraint conditions also include initial state constraint conditions, and the method includes: The initial state constraint conditions are determined according to the time information of the power-on process, the time information of the furnace loading process, the time information of the cooling process and the time information of the furnace unloading process in the electrode batch.

5. The method according to claim 3, characterized in that The power consumption related decision variables include the power supply related decision variables of the furnace group in the electrode batch. The constraint conditions are determined according to the scheduling characteristics of each process and the power consumption related decision variables, including: According to the time information of the power-on process of the furnace group in the electrode batch and the power-on related decision variables of the furnace group in the electrode batch, the power-on process constraints, the midway power-off operation constraints and the related variable constraints in the power-on process are determined.

6. The method according to claim 3, characterized in that The power consumption related decision variables include the power supply related decision variables of the furnace group in the electrode batch. The constraint conditions are determined according to the scheduling characteristics of each process and the power consumption related decision variables, including: The process constraints in the electrode batch are determined based on the electrode type and the time information of the designated process in the furnace group, the time information of the power-on process of the furnace group in the electrode batch, and the power-on-related decision variables of the furnace group in the electrode batch, wherein the designated process time information includes the start time, end time and duration of the process, and the designated process includes the furnace loading process, the cooling process and the furnace unloading process.

7. The method according to claim 3, characterized in that The electrode type and sub-furnace selection constraints for each electrode batch include: The constraint that each electrode batch can only process one electrode type; The constraint that each electrode batch can only work in one sub-furnace; The constraint that adjacent electrode batches cannot work in the same sub-furnace.

8. The method according to claim 3, characterized in that The decision variables related to the power consumption state include the electrode power decision variables. Constraints are determined based on the scheduling characteristics of each process and the decision variables related to the power consumption state, including: The electrode power constraints of each electrode batch in the power-on process are determined based on the power supply information of the transformer used by the sub-furnaces in each furnace group, the power curve of the corresponding processing electrode type when each sub-furnace is powered on, and the electrode power decision variables.

9. The method according to claim 1, characterized in that The constraints also include: The processing volume of each electrode must meet the total demand; The power consumption of all furnace groups at the same time cannot exceed the maximum total power threshold; All electrode batches must be processed before the maximum permissible completion time.

10. The method according to claim 1, characterized in that Obtaining scheduling parameters and solving the scheduling optimization model using the scheduling parameters includes: Obtain scheduling parameters, import the scheduling parameters into the scheduling optimization model, and use the CPLEX solver to perform optimization and solution, wherein the scheduling parameters include any one or any combination of scheduling time period, time-of-use electricity price information, furnace group parameters, time requirements of each process, and production characteristics of the power-on process.

11. A device for optimizing the scheduling of an electrode graphitization process, characterized in that: The device comprises: one or more processors; and A memory storing computer readable instructions which, when executed, cause the processor to perform the operations of the method of any one of claims 1 to 10.

12. A computer-readable medium having computer instructions stored thereon, wherein the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1 to 10.

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