Molten aluminum supply production line for low-pressure casting of aluminum alloy and control method of molten aluminum supply production line

Abstract

The invention relates to the field of intelligent manufacturing systems of aluminum alloy casting workshops, in particular to a molten aluminum supply production line for aluminum alloy low-pressure casting and a control method of the molten aluminum supply production line for aluminum alloy low-pressure casting. And the optimization model is solved in combination with the operation process and logic of the molten aluminum supply production line, so that the optimal solution of the operation parameters of the multiple devices is obtained. Besides, when the optimization model is solved, a pause probability model is introduced, and the optimal solution of the operation parameters under the target of achieving the minimum cost in the operation process of the molten aluminum supply process is obtained under the condition that pause is introduced. According to the aluminum liquid supply process proactive optimization method provided by the invention, the optimization model and the solving method based on the multi-agent simulation and meta-heuristic algorithm are constructed, so that the efficient optimization of the operation parameters is realized, and the purposes of reducing cost and improving efficiency are remarkably achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing systems for aluminum alloy casting workshops, specifically an aluminum liquid supply production line for low-pressure aluminum alloy casting and its control method. Background Technology

[0002] Low-pressure casting is a commonly used process for manufacturing aluminum alloy castings, widely applied in the production of various load-bearing structural components such as automotive wheels and subframes. In the low-pressure casting process, the aluminum molten material supply step includes melting and holding, refining, distribution, and further holding. This step accounts for approximately 60% of the total energy consumption and 70% of the total carbon emissions in aluminum casting manufacturing, making it a core component for achieving intelligent and low-carbon operation. Related research indicates that optimizing the operating parameters of the aluminum molten material supply step can save 10% to 20% of energy consumption, resulting in significant economic benefits.

[0003] To ensure a stable supply of molten aluminum and reduce manual labor, companies typically use fixed operating parameters for the aluminum supply in actual production. These parameters are mainly set as the rated melting rate (i.e., maximum melting rate), the upper and lower limits of the maximum holding zone in the melting furnace, and the rated aluminum supply volume (i.e., the maximum capacity of the holding furnace). While this commonly used parameter setting can optimize some indicators, such as reducing refining, transportation, and furnace changing times, and extending transportation intervals, it often leads to excessive energy consumption during holding, resulting in energy waste. Furthermore, occasional production stoppages can cause the molten aluminum to remain in the casting unit beyond its storage time limit, requiring it to be transported back for reprocessing, adding extra workload.

[0004] The operating parameters of molten aluminum supply all affect melting efficiency, melting loss, melting and holding energy consumption, molten aluminum shortage in the melting furnace, refining cycles, refining and holding energy consumption, transportation cycles, furnace changeover cycles, production downtime caused by furnace changeovers, holding energy consumption in the casting area, and the remelting of molten aluminum due to deterioration over time. These effects have both positive and negative relationships. Decoupling these complex relationships and finding an optimal set of parameters is a challenge facing the industry. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an aluminum liquid supply production line for low-pressure casting of aluminum alloys and its control method, so as to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The present invention provides an aluminum liquid supply production line for low-pressure casting of aluminum alloys, comprising:

[0008] A multi-equipment and aluminum liquid supply scheduling system, wherein the multi-equipment includes melting furnaces, holding furnaces, aluminum liquid refining equipment, ground-rail aluminum liquid transport tracks, transport vehicles, and casting units:

[0009] The aluminum liquid supply scheduling system is used to determine the optimal solution of various operating parameters based on the pre-input production plan and the status parameters of various equipment. The various operating parameters include the melting rate of the melting furnace, the upper limit of the holding temperature of the melting furnace, the lower limit of the holding temperature of the melting furnace, the set of new furnace aluminum liquid volume for each casting unit, and the set of furnace changeover time for each casting unit.

[0010] This application also provides a method for controlling the supply of molten aluminum in low-pressure casting of aluminum alloys, including:

[0011] Obtain production plans and operating parameters for various equipment;

[0012] Determine the optimal solution for various operating parameters based on the pre-input production plan and the status parameters of various equipment;

[0013] Controlling the aluminum liquid supply production line based on the optimal solution of operating parameters.

[0014] The beneficial effects of this invention are as follows: This invention provides an aluminum liquid supply production line and its control method for low-pressure casting of aluminum alloys, comprising various equipment and an aluminum liquid supply scheduling system. This application constructs an optimization model through the aluminum liquid supply scheduling system, and solves the optimization model by combining the operation flow and logic of the aluminum liquid supply production line, thereby obtaining the optimal solutions for the operating parameters of various equipment. Furthermore, when solving the optimization model, a pause probability model is introduced to obtain the optimal solution for the operating parameters under the condition of pausing, achieving the objective of minimizing the operating cost of the aluminum liquid supply process. The proactive optimization method for the aluminum liquid supply process proposed in this application, by constructing an optimization model and solution method based on multi-agent simulation and metaheuristic algorithms, achieves efficient optimization of operating parameters, significantly achieving the goal of cost reduction and efficiency improvement.

[0015] This application proactively considers the impact of potential production stoppages on operational optimization and proposes a proactive optimization strategy to address production stoppages in advance, which significantly enhances the overall robustness of the system.

[0016] This application has significant industrial application value and can be seamlessly integrated into the Manufacturing Execution System (MES) of an enterprise's aluminum alloy low-pressure casting workshop. This system can automatically and in real-time calculate the optimal operating parameter configuration for different production plans. This function not only effectively improves the intelligence level of the production line operation but also substantially reduces the overall operating cost of the production line. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0018] Figure 1 This is a hardware structure diagram of an aluminum liquid supply production line for low-pressure casting of aluminum alloys, shown in one embodiment of this application.

[0019] Figure 2 This is a logic diagram of an aluminum liquid supply production line for low-pressure casting of aluminum alloys, shown in one embodiment of this application.

[0020] Figure 3 This is a diagram illustrating the operation method of an aluminum liquid supply production line for low-pressure casting of aluminum alloys, as shown in one embodiment of this application.

[0021] Figure 4 This is a flowchart illustrating an aluminum liquid supply control method for low-pressure casting of aluminum alloys, as shown in one embodiment of this application. Detailed Implementation

[0022] Figure 1 This is a hardware structure diagram of an aluminum liquid supply production line for low-pressure casting of aluminum alloys, as shown in one embodiment of this application. Figure 1 As shown, it includes:

[0023] The system includes a variety of equipment and an aluminum liquid supply scheduling system, including melting furnaces, holding furnaces, aluminum liquid refining equipment, ground-rail aluminum liquid transport tracks, transport vehicles, and casting units.

[0024] Aluminum molten metal refining equipment is used to house the holding furnace and to refine the aluminum molten metal inside the holding furnace.

[0025] The transport vehicle runs along a ground-rail aluminum molten material transport track to transport the holding furnace containing refined aluminum molten material to the casting unit; the casting unit is used for low-pressure casting based on the refined aluminum molten material; the transport vehicle is also used to transport the empty holding furnace back to the aluminum molten material refining equipment when the aluminum molten material in the holding furnace is exhausted.

[0026] The aluminum liquid supply scheduling system is used to determine the optimal solution of various operating parameters based on the pre-input production plan and the status parameters of various equipment. These operating parameters include the melting rate of the melting furnace, the upper limit of the holding capacity of the melting furnace, the lower limit of the holding capacity of the melting furnace, the set of new furnace aluminum liquid volume for each casting unit, and the set of furnace changeover times for each casting unit.

[0027] Specifically, the aluminum molten metal supply production line in this embodiment includes an aluminum alloy tower-type centralized melting furnace, multiple aluminum molten metal refining devices, a ground-rail aluminum molten metal transport track, a transport vehicle, multiple casting units, an industrial control computer, and an aluminum molten metal supply scheduling system. The aluminum alloy tower-type centralized melting furnace is equipped with a precise discharge device; the aluminum molten metal refining devices are equipped with degassing machines, aluminum molten metal heat preservation devices, and other equipment; the casting units include low-pressure casting machines, aluminum molten metal heat preservation furnaces, and part removal machines.

[0028] The aluminum alloy tower-type centralized melting furnace, aluminum liquid refining equipment, and casting unit are all interconnected with the industrial control computer via network cables to achieve data communication. The transport vehicles are connected to the industrial control computer via a wireless network to achieve information exchange. The industrial control computer can obtain the real-time operating status of each connected device and issue commands to each device. The aluminum liquid supply scheduling system runs on the industrial control computer, which is also interconnected with the enterprise information system to receive production plans and provide feedback on production progress and other information.

[0029] In the operation of the aluminum liquid supply system of the low-pressure casting production line, an aluminum alloy tower-type centralized melting furnace melts the aluminum alloy and heats the molten aluminum to a set temperature for holding. Subsequently, the tower-type centralized melting furnace quantitatively adds molten aluminum to the holding furnace located in the aluminum liquid refining equipment. After undergoing degassing, slag removal, and remodeling treatments at the refining station, the molten aluminum is transported by an aluminum liquid transport vehicle via a ground-rail transport track to each casting unit. Once the molten aluminum is used up, the casting unit unloads the old holding furnace onto the transport vehicle and installs a new holding furnace. The transport vehicle then returns the old holding furnace to the refining station. The entire aluminum liquid supply system operates automatically under the coordination of the scheduling system and the automatic control systems of each piece of equipment.

[0030] In the above logical process, the operating parameters of the entire aluminum liquid supply production line are determined: the melting rate of the melting furnace, the upper limit of the holding capacity of the melting furnace, the lower limit of the holding capacity of the melting furnace, the set of new furnace aluminum liquid volume of each casting unit, and the set of furnace changeover time of each casting unit. These are the optimization parameters for which the optimal solution is to be obtained in this application.

[0031] Figure 2 This is a logic diagram of an aluminum liquid supply production line for low-pressure casting of aluminum alloys, shown in one embodiment of this application. Figure 3 This is a diagram illustrating the operation method of an aluminum liquid supply production line for low-pressure casting of aluminum alloys, as shown in one embodiment of this application. Please refer to the diagram. Figures 2-3 As shown, the operating logic of the aluminum liquid supply production line includes:

[0032] The acquisition module 210 is used to acquire historical production suspension datasets of multiple low-pressure casting units and historical aluminum liquid usage datasets of the holding furnace corresponding to the historical production suspension data;

[0033] Specifically, the duration and start time of each historical production stoppage in N low-pressure casting units are collected from the enterprise's information system to obtain a dataset of total production stoppage durations. The dataset of the start time of total production pauses , where N is an integer greater than or equal to 1.

[0034] These are the data sets of production pause durations from the 1st to the Nth casting unit.

[0035] These are the sets of times when production pauses begin from the 1st to the Nth casting unit.

[0036] In addition, datasets of production downtime are collected from the company's information systems. The starting time and duration of the molten aluminum in the holding furnace for each data point are used to obtain the total dataset of the starting times of the molten aluminum in the holding furnace. and usage duration dataset ;

[0037] These are data sets showing the start times of use of molten aluminum in the holding furnaces for casting units from the 1st to the Nth.

[0038] These are data sets of the usage time of molten aluminum in the holding furnace from the 1st to the Nth casting unit.

[0039] Each of the above sets includes corresponding data generated during multiple production processes, for example... The set contains multiple production pause durations for low-pressure casting unit 1.

[0040] The probability distribution determination module 220 is used to construct a first probability density distribution set of the production pause duration ratio of each low-pressure casting unit based on the duration of multiple production pauses and the usage time of the aluminum liquid in the holding furnace corresponding to the multiple production pauses, and to construct a second probability density distribution set of the production pause start time of each low-pressure casting unit based on the start time of multiple production pauses and the start time of the use of the aluminum liquid in the holding furnace corresponding to the multiple production pauses.

[0041] In this application, the optimization problem needs to incorporate the possibility of random pauses in low-pressure casting units. To characterize the start time and duration of these random pauses, this application constructs a first probability density distribution set of the proportion of production pause duration for each low-pressure casting unit and a second probability density distribution set of the start time of production pauses for each low-pressure casting unit, specifically including:

[0042] (1) For each low-pressure casting unit, calculate the ratio of the duration of multiple production pauses to the usage time of the corresponding holding furnace aluminum liquid to obtain the production pause duration ratio dataset; and calculate the difference between the start time of multiple production pauses and the start time of the corresponding holding furnace aluminum liquid to obtain the production pause start time dataset.

[0043] Specifically, the dataset of production downtime percentages was calculated. and production pause start time dataset ;

[0044] , These are the sets of production downtime percentages from the 1st to the Nth casting unit;

[0045] , These are datasets representing the start times of production pauses from the 1st to the Nth casting unit;

[0046] (2) Estimate the production pause duration percentage dataset based on the kernel density estimation method to obtain the first probability density distribution set of the production pause duration percentage of each low-pressure casting unit; and estimate the production pause start time dataset based on the kernel density estimation method to obtain the second probability density distribution set of the production pause start time of each low-pressure casting unit.

[0047] The probability density distribution set of the production downtime percentage for each casting unit was calculated using a kernel density estimation method. and the probability density distribution set of the start time of production pause for each casting unit ;

[0048] in, These are the percentage of production pause time for the Nth casting unit and the probability density distribution functions for the start time of production pause for each casting unit, respectively. The calculation process includes data cleaning, kernel function selection, bandwidth optimization, and density calculation.

[0049] The optimization model construction module 230 is used to construct an optimization model with multiple operating constraints, aiming to minimize the total cost of the aluminum liquid supply process. The total cost of the aluminum liquid supply process includes the operating cost of the melting furnace, the operating cost of the aluminum liquid refining area, the aluminum liquid transportation cost, and the operating cost of the casting unit.

[0050] This application constructs an optimization model to calculate the optimal solutions for various operating parameters that minimize the total cost of the aluminum liquid supply process.

[0051] (1) The goal of the optimization model is to minimize the total operating cost of the aluminum liquid supply process. Total operating costs Operating costs of the melting furnace Operating costs of the aluminum liquid refining area Aluminum liquid transportation costs 1. Casting unit operating cost composition.

[0052] (1-1) Operating costs of the melting furnace

[0053] The operating costs include the energy consumption costs of the melting burners in the melting furnace. Cost of aluminum alloy melting and burning loss in melting furnace Energy consumption cost of insulated burners in the insulation zone of the melting furnace Losses due to production stoppage caused by insufficient supply of molten aluminum to the melting furnace ;

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, The unit price of energy used for melting in the melting furnace. This refers to the unit price of aluminum alloy in the melting furnace. The unit price for losses due to the suspension of production at the melting furnace; These are known parameters;

[0059] This is the first coefficient relating the melting power of the melting furnace to the melting rate. This is the second coefficient in the relationship between the melting power and melting rate of the melting furnace. This is the third coefficient in the relationship between the melting power and melting rate of the melting furnace. This is the first coefficient relating the heat preservation power of the heat preservation zone in the melting furnace to the amount of molten aluminum in the heat preservation zone. This is the second coefficient relating the melting loss rate and melting speed in a melting furnace. This is the first coefficient relating the heat preservation power of the heat preservation zone in the melting furnace to the amount of molten aluminum in the heat preservation zone. This is the second coefficient relating the heat preservation power of the heat preservation zone in the melting furnace to the amount of molten aluminum in the heat preservation zone; , It can be obtained through experimentation and fitting.

[0060] The working time of the melting burner in the melting furnace. This refers to the standby time of the melting burner in the melting furnace. This refers to the standby power of the melting burner in the melting furnace. These represent the energy loss during a single melting state transition in the melting furnace. This refers to the number of times the melting state of the melting furnace is switched. The duration of heat preservation in the heat preservation zone of the melting furnace. The volume of molten aluminum in the holding zone of the melting furnace. For the first The duration of production stoppage in a casting unit due to untimely supply of molten aluminum; This refers to the melting speed of the melting furnace. , It can be obtained through experimentation.

[0061] (1-2) Operating costs of the aluminum liquid refining area

[0062]

[0063] In the formula, The unit price of energy used for insulation in the refining area. This refers to the unit price of materials consumed in the refining process within the refining zone. Process the cost of one refining operation for the personnel in the refining area; These are known parameters.

[0064] This is a coefficient representing the relationship between the heat preservation energy consumption in the refining zone and the refining throughput. This is a coefficient relating the material consumption to the refining throughput in the refining process. It can be obtained through experimentation and fitting;

[0065] This represents the total number of refining attempts. This represents the total amount of molten aluminum refined in the i-th casting unit; This refers to the number of low-pressure casting units.

[0066] (1-3) Liquid transportation costs

[0067]

[0068] In the formula, The cost of one transportation is a known parameter. The number of transports in the process.

[0069] (1-4) Operating cost of casting unit

[0070]

[0071] In the formula, For the first Number of furnace changes per casting unit;

[0072] The unit price of heat preservation energy consumption for casting units. The unit price at which production is halted due to a single furnace change; are known parameters;

[0073] For the first The first casting unit Usage time after the first furnace replacement For the first The first casting unit Real-time aluminum liquid volume after the first furnace change;

[0074] This is the first coefficient relating the insulation power and insulation amount of the insulated furnace in the casting area. This is the second coefficient relating the insulation power and insulation amount in the casting zone's holding furnace. It can be obtained through experimentation and fitting.

[0075] In the calculation expression of the objective function of the above optimization model, apart from the known parameters and the parameters that can be obtained through experiments, the other parameters are obtained based on discrete event simulation. During experimental fitting, the above production line is used as the experimental equipment to supply molten aluminum, and data of experimental parameters from multiple experiments are collected. The specific values ​​can be obtained through fitting. The simulation method is as follows.

[0076] This paper analyzes the operational logic and constraints of the aluminum molten metal supply process in low-pressure aluminum alloy casting, and extracts the inputs, outputs, and actions of multiple intelligent agents, including melting furnace agents, refining agents, transportation agents, and casting agents. Based on the logical relationships of the production process, a mechanism for communication and cooperation among these multiple agents is established. The specific logical relationships of the production process are described in the preceding section on the production line.

[0077] By using code programming or commercial discrete event simulation software, multiple intelligent agents in the aluminum liquid supply process are encoded and assembled based on the logical relationships of workshop operation into an executable aluminum liquid supply process simulation code module that can simulate the operation process of the aluminum liquid supply process with high fidelity.

[0078] The simulation code module for the aluminum liquid supply process is executed, taking the production plan, the operating parameters of the aluminum liquid supply process, and the initial state as inputs. During the simulation, the program records key data, and at the end of the simulation, the parameters that need to be calculated in the objective function of the optimization model are obtained based on the recorded data and the simulation results.

[0079] (2) The optimization variables of the optimization model include: the melting rate of the melting furnace. The upper limit of the insulation amount of the melting furnace, the lower limit of the insulation amount of the melting furnace, the set of new furnace aluminum liquid volume for each casting unit, and the set of furnace changeover times for each casting unit.

[0080] Among the optimization variables, only the melting rate has a direct relationship with the objective function; the other variables have indirect relationships with the objective function. Since the aluminum liquid supply process is discrete, a corresponding mathematical model cannot be established. Therefore, this application establishes a simulation model to replace the mathematical model of this relationship, that is, to establish the indirect relationship between the other variables and the objective function through simulation.

[0081] (3) The constraints of the optimization model include:

[0082] (3-1) Melting speed of the melting furnace Between the minimum and maximum adjustable melting speeds of the melting furnace;

[0083] (3-2) The upper limit of the insulation of the melting furnace is greater than the lower limit of the insulation of the melting furnace; the upper limit of the insulation of the melting furnace is greater than the minimum working insulation of the melting furnace, and the upper limit of the insulation of the melting furnace is less than the maximum insulation of the melting furnace; the lower limit of the insulation of the melting furnace is greater than the minimum working insulation of the melting furnace, and the lower limit of the insulation of the melting furnace is less than the maximum insulation of the melting furnace.

[0084] (3-3) The amount of new aluminum liquid in each casting unit is greater than the minimum working amount of aluminum liquid, and the amount of new aluminum liquid in each casting unit is less than the maximum aluminum liquid capacity of the holding furnace.

[0085] (4) In addition to the operating parameters, the input parameters of the optimization model also include the production plan and the initial state, specifically:

[0086] (4-1) Production Planning

[0087] (4-1-1) The number of products required to be produced by each casting unit;

[0088] (4-1-2) The pouring volume and pouring interval for each casting unit;

[0089] (4-2) Initial state

[0090] (4-2-1) The amount of aluminum liquid kept in the heat preservation zone of the melting furnace;

[0091] (4-2-2) The amount of molten aluminum in the holding furnace of each casting unit;

[0092] (4-2-3) Status of the aluminum liquid refining area: whether there is a holding furnace in the process of refining, the duration of refining, and the amount of aluminum liquid being refined;

[0093] (4-2-4) Status of the transport vehicle: whether it is currently transporting, and the number of the casting unit it is transporting to.

[0094] The solution module 240 is used to solve the optimization model by combining the first probability density distribution set and the second probability density distribution set to obtain the optimal solution for various operating parameters of the aluminum liquid supply process.

[0095] For the optimization model constructed above, this application performs the solution using the following method:

[0096] (1) A random pause is introduced into the production process of each low-pressure casting unit after each furnace change, wherein the random pause includes the pause start time that follows the second probability density distribution set and the pause duration that follows the first probability density distribution set;

[0097] The simulation code module for the aluminum liquid supply process based on multi-agent simulation established above is modified to randomly add a production pause to each casting unit after each furnace change. The start time of the random production pause is set to follow a probability density function. The distribution of random production pause durations is set to follow a probability density function as follows: The distribution of .

[0098] (2) The optimization model is solved based on the particle swarm optimization algorithm to obtain the optimal solutions for various operating parameters of the aluminum liquid supply process, wherein the various operating parameters include the melting rate of the melting furnace. The upper limit of the insulation amount of the melting furnace, the lower limit of the insulation amount of the melting furnace, the set of new furnace aluminum liquid volume for each casting unit, and the set of furnace changeover time for each casting unit.

[0099] The particle swarm optimization algorithm is used to solve the optimization model. When calculating the fitness value of the swarm, a parallel computing method is employed, evenly distributing the swarm across multiple computational cores for calculation, and finally, the results are aggregated. To calculate the fitness of each particle, the fitness of each particle is calculated under M (a large integer) combinations of uncertain production pause times and durations in the casting units. The fitness distribution of this particle is then obtained, and the expected value of this distribution is taken as the particle's fitness value.

[0100] The following is an implementation example provided in this application:

[0101] In this case, a production line of a certain enterprise consists of a furnace and four low-pressure casting units. A certain production plan is to produce four products, which requires setting the operating parameters of the aluminum liquid supply. The following describes how to use the present invention to obtain the optimal operating parameters of this production plan.

[0102] (1) More than 600 production stoppage data of four casting units in the past month were collected from the enterprise's manufacturing execution system. The data were preprocessed and the probability density function of the time and duration of production stoppage for each casting unit was obtained by kernel density estimation.

[0103] (2) Establish a simulation model of the production line in Matlab to achieve the output of the parameters required for the optimization model given input. In addition, random production pauses are added to the simulation model, and the pause time and duration need to follow the probability density distribution obtained in step 1.

[0104] (3) In Matlab, the optimization model is encoded and the particle swarm algorithm is encoded. The optimization model needs to call the simulation module, and the encoded particle swarm algorithm supports parallel computing.

[0105] (4) Collect the static parameters required for the model from the enterprise, including price, equipment attribute parameters, model coefficients, etc., and input them into the software module of step (3) as basic data.

[0106] (5) Input the production plan into the calculation software module developed in the previous step, run the software, and obtain a set of optimal parameters.

[0107] (6) The enterprise produces according to the default operating parameters, and after the production is completed, it calculates the operating cost of this aluminum liquid supply process.

[0108] (7) The production stoppage events encountered in the actual production process of the enterprise are encoded and embedded into the simulation model of step (2), and the operating cost of the aluminum liquid supply process with the optimal parameters obtained by the present invention is calculated. Then, the operating cost of the aluminum liquid supply process with the parameters obtained by the present invention is compared with the operating cost of the parameters currently used by the enterprise. The results show that the present invention has achieved a cost reduction of 13%.

[0109] Figure 4 This is a flowchart of an aluminum liquid supply control method for low-pressure casting of aluminum alloys according to an embodiment of this application, such as... Figure 4 As shown, this application also provides a method for controlling the supply of molten aluminum in low-pressure casting of aluminum alloys, comprising the following steps:

[0110] (1) Obtain production plans and operating parameters of various equipment;

[0111] This application runs on an industrial control computer, which receives the aluminum casting production plan, including: the product demand quantity for each casting unit, the pouring volume and pouring interval for each unit, and the aluminum liquid volume in the holding furnace of each unit. The industrial control computer also collects the status parameters of each piece of equipment in the low-pressure casting production line in real time, including: the aluminum liquid volume in the holding zone of the melting furnace, the status of the aluminum liquid refining zone (whether there is a holding furnace currently refining, the refining time, and the amount of refined aluminum liquid), and the status of the transport vehicle (whether it is in transit, and the current target casting unit number), etc.

[0112] (2) Determine the optimal solution for various operating parameters based on the pre-input production plan and the status parameters of various equipment;

[0113] When determining the optimal solution, the historical production suspension datasets of multiple low-pressure casting units and the historical holding furnace aluminum liquid usage datasets corresponding to the historical production suspension datasets are obtained. The historical production suspension datasets include the duration and start time of multiple production suspensions, and the historical holding furnace aluminum liquid usage datasets include the start time and usage duration of the holding furnace aluminum liquid corresponding to multiple production suspensions.

[0114] Based on the duration of multiple production pauses and the usage time of the aluminum liquid in the holding furnace corresponding to the multiple production pauses, a first probability density distribution set of the production pause duration ratio of each low-pressure casting unit is constructed, and based on the start time of multiple production pauses and the start time of the use of the aluminum liquid in the holding furnace corresponding to the multiple production pauses, a second probability density distribution set of the production pause start time of each low-pressure casting unit is constructed.

[0115] An optimization model is constructed with the goal of minimizing the total cost of the aluminum liquid supply process and various operating constraints. The total cost of the aluminum liquid supply process includes the operating cost of the melting furnace, the operating cost of the aluminum liquid refining area, the aluminum liquid transportation cost, and the operating cost of the casting unit.

[0116] By combining the first probability density distribution set and the second probability density distribution set, the optimization model is solved to obtain the optimal solutions for various operating parameters of the aluminum liquid supply process.

[0117] (3) Control the aluminum liquid supply production line based on the optimal solution of various operating parameters.

[0118] Specifically, the industrial control computer takes the above parameters as input, runs the program module developed based on the method for determining aluminum liquid supply process parameters proposed in this invention, calculates the melting rate of the melting furnace, the upper and lower limits of the aluminum liquid volume in the heat preservation zone, the set of new furnace aluminum liquid volume and the set of furnace change time for each casting unit, and transmits the result parameters to the aluminum liquid supply scheduling system.

[0119] The aluminum liquid supply scheduling system sends parameters such as the melting rate of the melting furnace and the upper and lower limits of the aluminum liquid volume in the holding zone to the aluminum alloy tower-type centralized melting and holding furnace, which is then automatically controlled by its PLC. Based on the new furnace aluminum liquid volume and furnace changeover time of each casting unit, the scheduling system issues timely instructions for the discharge time and volume to the tower-type centralized melting and holding furnace, notifies the aluminum liquid refining station to perform refining processing, and simultaneously instructs the aluminum liquid transport vehicle to transport the holding furnace to the designated casting unit for furnace changeover operation.

[0120] This invention discloses an aluminum liquid supply production line and its control method for low-pressure aluminum alloy casting. This application analyzes historical production stoppage data and historical aluminum liquid usage datasets from multiple low-pressure casting units to obtain the probability distributions of the start and duration of production stoppages in multiple low-pressure casting units. Then, an optimization model based on multi-agent simulation is established to minimize the operating cost of the aluminum liquid supply process. By incorporating the probability distributions of the start and duration of production stoppages in multiple low-pressure casting units and solving the optimization model, the optimal solution for the operating parameters under the condition of introducing stoppages is obtained, achieving the goal of minimizing the operating cost of the aluminum liquid supply process. The proactive optimization method for the aluminum liquid supply process proposed in this application, through the construction of an optimization model and solution method based on multi-agent simulation and metaheuristic algorithms, achieves efficient optimization of operating parameters, significantly achieving the goal of cost reduction and efficiency improvement.

[0121] This application proactively considers the impact of potential production stoppages on operational optimization and proposes a proactive optimization strategy to address production stoppages in advance, which significantly enhances the overall robustness of the system.

[0122] This application has significant industrial application value and can be seamlessly integrated into the Manufacturing Execution System (MES) of an enterprise's aluminum alloy low-pressure casting workshop. This system can automatically and in real-time calculate the optimal operating parameter configuration for different production plans. This function not only effectively improves the intelligence level of the production line operation but also substantially reduces the overall operating cost of the production line.

Claims

1. An aluminum molten metal supply production line for low-pressure casting of aluminum alloys, characterized in that, This includes various equipment and an aluminum liquid supply scheduling system, wherein the various equipment includes melting furnaces, holding furnaces, aluminum liquid refining equipment, ground-rail aluminum liquid transport tracks, transport vehicles, and casting units: The aluminum liquid supply scheduling system is used to determine the optimal solution of various operating parameters based on the pre-input production plan and the status parameters of various equipment. The various operating parameters include the melting rate of the melting furnace, the upper limit of the holding temperature of the melting furnace, the lower limit of the holding temperature of the melting furnace, the set of new furnace aluminum liquid volume for each casting unit, and the set of furnace changeover time for each casting unit.

2. The aluminum liquid supply production line for low-pressure casting of aluminum alloys according to claim 1, characterized in that, Determine the optimal solutions for various operating parameters based on pre-input production plans and the status parameters of multiple equipment, including: Acquire historical production pause datasets for multiple low-pressure casting units and historical holding furnace molten aluminum usage datasets corresponding to the historical production pause datasets. The historical production pause datasets include the duration and start time of multiple production pauses, and the historical holding furnace molten aluminum usage datasets include the start time and duration of molten aluminum usage in the holding furnace corresponding to multiple production pauses. Based on the duration of multiple production pauses and the usage time of the aluminum liquid in the holding furnace corresponding to the multiple production pauses, a first probability density distribution set of the production pause duration ratio of each low-pressure casting unit is constructed, and based on the start time of multiple production pauses and the start time of the use of the aluminum liquid in the holding furnace corresponding to the multiple production pauses, a second probability density distribution set of the production pause start time of each low-pressure casting unit is constructed. An optimization model is constructed under the aforementioned production plan, with the goal of minimizing the total cost of the aluminum liquid supply process, the constraints of the available range of various operating parameters, and the optimization variables of various operating parameters. The total cost of the aluminum liquid supply process includes the operating cost of the melting furnace, the operating cost of the aluminum liquid refining area, the aluminum liquid transportation cost, and the operating cost of the casting unit. By combining the first probability density distribution set and the second probability density distribution set, the optimization model is solved to obtain the optimal solutions for various operating parameters of the aluminum liquid supply process.

3. The aluminum liquid supply production line for low-pressure casting of aluminum alloys according to claim 2, characterized in that, Based on the duration of multiple production pauses and the usage time of the molten aluminum in the holding furnace corresponding to each pause, a first probability density distribution set of the production pause duration ratio for each low-pressure casting unit is constructed. Furthermore, based on the start time of multiple production pauses and the start time of the molten aluminum in the holding furnace corresponding to each pause, a second probability density distribution set of the production pause start time for each low-pressure casting unit is constructed, including: For each low-pressure casting unit, the ratio of the duration of multiple production pauses to the usage time of the corresponding holding furnace aluminum liquid is calculated to obtain the production pause duration ratio dataset; and the difference between the start time of multiple production pauses and the start time of the corresponding holding furnace aluminum liquid is calculated to obtain the production pause start time dataset. The production pause duration percentage dataset is estimated using the kernel density estimation method to obtain the first probability density distribution set of the production pause duration percentage for each low-pressure casting unit; and the production pause start time dataset is estimated using the kernel density estimation method to obtain the second probability density distribution set of the production pause start time for each low-pressure casting unit.

4. The aluminum liquid supply production line for low-pressure casting of aluminum alloys according to claim 2, characterized in that, The operating costs include the energy consumption costs of the melting burners in the melting furnace. Cost of aluminum alloy melting and burning loss in melting furnace Energy consumption cost of insulated burners in the insulation zone of the melting furnace Losses due to production stoppage caused by insufficient supply of molten aluminum to the melting furnace ,in, , , and The mathematical expressions are as follows: ； ； ； ； In the formula, The unit price of energy used for melting in the melting furnace. This refers to the unit price of aluminum alloy in the melting furnace. The unit price for losses due to the suspension of production at the melting furnace; This is the first coefficient relating the melting power of the melting furnace to the melting rate. This is the second coefficient in the relationship between the melting power and melting rate of the melting furnace. This is the third coefficient in the relationship between the melting power and melting rate of the melting furnace. This is the first coefficient relating the heat preservation power of the heat preservation zone in the melting furnace to the amount of molten aluminum in the heat preservation zone. This is the second coefficient relating the melting loss rate and melting speed in a melting furnace. This is the first coefficient relating the heat preservation power of the heat preservation zone in the melting furnace to the amount of molten aluminum in the heat preservation zone. This is the second coefficient relating the heat preservation power of the heat preservation zone in the melting furnace to the amount of molten aluminum in the heat preservation zone; The working time of the melting burner in the melting furnace. This refers to the standby time of the melting burner in the melting furnace. This refers to the standby power of the melting burner in the melting furnace. These represent the energy loss during a single melting state transition in the melting furnace. This refers to the number of times the melting state of the melting furnace is switched. The duration of heat preservation in the heat preservation zone of the melting furnace. The volume of molten aluminum in the holding zone of the melting furnace. For the first The duration of production stoppage in a casting unit due to untimely supply of molten aluminum; The melting rate of the melting furnace; Operating costs of the aluminum molten metal refining zone The mathematical expression is: ； In the formula, The unit price of energy used for insulation in the refining area. This refers to the unit price of materials consumed in the refining process within the refining zone. To process the cost of one refining operation for the personnel in the refining area; This is a coefficient representing the relationship between the heat preservation energy consumption in the refining zone and the refining throughput. This is a coefficient relating the material consumption to the refining throughput in the refining process. This represents the total number of refining attempts. This represents the total amount of molten aluminum refined in the i-th casting unit; The number of low-pressure casting units. The cost of transporting molten aluminum The mathematical expression is: ； In the formula, The cost of one shipment; For the number of transports; The operating cost of the casting unit The mathematical expression is: ； In the formula, For the first Number of furnace changes per casting unit The unit price of heat preservation energy consumption for casting units. The unit price for a production halt caused by a single furnace change; For the first The first casting unit Usage time after the first furnace replacement For the first The first casting unit Real-time aluminum liquid volume after the second furnace change; This is the first coefficient relating the insulation power and insulation amount of the insulated furnace in the casting area. It is the second coefficient relating the heat preservation power and heat preservation amount of the heat preservation furnace in the casting area.

5. The aluminum liquid supply production line for low-pressure casting of aluminum alloys according to claim 3, characterized in that, The first coefficient The second coefficient The third coefficient The first coefficient The second coefficient The first coefficient The second coefficient The standby power of the melting burner in the melting furnace Energy loss during a single melting state switch in the melting furnace The coefficient relating the heat preservation energy consumption in the refining zone to the refining throughput. The coefficient relating material consumption to refining throughput in the refining process. The first coefficient and the second coefficient All of these are experimental fitting parameters, which are obtained through experimental fitting. The standby time of the melting burner in the melting furnace The working time of the melting burner in the melting furnace Number of times the melting state of the melting furnace is switched Quantity of molten aluminum in the holding zone of the melting furnace , No. Production stoppage time in one casting unit due to untimely aluminum liquid supply The number of transports The first Number of furnace changes per casting unit , No. The first casting unit Service life after the second furnace replacement and the first The first casting unit The real-time aluminum liquid volume after the furnace change is a simulation parameter, which is obtained through a pre-constructed multi-agent simulation. The unit price of energy used for melting in the melting furnace The unit price of aluminum alloy in the melting furnace The unit price of loss due to production stoppage of the aforementioned melting furnace The unit price of energy used for heat preservation in the refining area The unit price of refining process materials consumed in the refining zone The cost of one refining operation by personnel in the refining area. The cost of one shipment The unit price of heat preservation energy consumption of the casting unit And the unit price for production suspension caused by a single furnace change. All parameters are known.

6. The aluminum liquid supply production line for low-pressure casting of aluminum alloys according to claim 2, characterized in that, The multiple constraints include: Melting speed of the melting furnace Between the minimum and maximum adjustable melting speeds of the melting furnace; The upper limit of the insulation amount for the melting furnace is greater than the lower limit of the insulation amount for the melting furnace; the upper limit of the insulation amount for the melting furnace is greater than the minimum working insulation amount for the melting furnace, and the upper limit of the insulation amount for the melting furnace is less than the maximum insulation amount for the melting furnace; the lower limit of the insulation amount for the melting furnace is greater than the minimum working insulation amount for the melting furnace, and the lower limit of the insulation amount for the melting furnace is less than the maximum insulation amount for the melting furnace. The amount of new molten aluminum in each casting unit is greater than the minimum working amount of molten aluminum, and the amount of new molten aluminum in each casting unit is less than the maximum molten aluminum capacity of the holding furnace.

7. The aluminum liquid supply production line for low-pressure casting of aluminum alloys according to claim 5, characterized in that, The method for determining the simulation parameters is as follows: A multi-agent simulation model is constructed based on the operation logic and constraints of the aluminum liquid supply production line. The multi-agent simulation model includes a melting furnace agent, a refining agent, a transportation agent, and a casting agent. The production plan, the operating parameters of various equipment, and the initial states of various equipment are input into the multi-agent simulation model to obtain simulation parameters.

8. The aluminum liquid supply production line for low-pressure casting of aluminum alloys according to claim 1, characterized in that, By combining the first probability density distribution set and the second probability density distribution set to solve the optimization model, optimal solutions for various operating parameters of the aluminum liquid supply process are obtained, including: For each low-pressure casting unit, a random pause is introduced during the production process after each furnace change. The random pause includes a pause start time that follows the second probability density distribution set and a pause duration that follows the first probability density distribution set. The optimization model is solved using the particle swarm optimization algorithm to obtain optimal solutions for various operating parameters of the aluminum molten metal supply process. These parameters include the melting rate of the melting furnace. The upper limit of the insulation amount of the melting furnace, the lower limit of the insulation amount of the melting furnace, the set of new furnace aluminum liquid volume for each casting unit, and the set of furnace changeover time for each casting unit.

9. The aluminum liquid supply production line for low-pressure casting of aluminum alloys according to claim 1, characterized in that, The melting furnace is used to melt and hold the aluminum alloy to obtain molten aluminum; and is used to inject the molten aluminum into the holding furnace of the molten aluminum refining equipment; The aluminum liquid refining equipment is used to place the holding furnace and to refine the aluminum liquid inside the holding furnace. The transport vehicle runs along a ground-rail aluminum molten material transport track to transport the holding furnace containing refined aluminum molten material to the casting unit; the casting unit is used for low-pressure casting based on the refined aluminum molten material; the transport vehicle is also used to transport the empty holding furnace back to the aluminum molten material refining equipment when the aluminum molten material in the holding furnace is exhausted.

10. A method for controlling the supply of molten aluminum in low-pressure casting of aluminum alloys, characterized in that, include: Obtain production plans and operating parameters for various equipment; Determine the optimal solution for various operating parameters based on the pre-input production plan and the status parameters of various equipment; Controlling the aluminum liquid supply production line based on the optimal solution of operating parameters.

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