Method and system for controlling energy consumption in aluminum profile extrusion process

Abstract

The invention relates to an aluminum profile extrusion process energy consumption control method and system, and the method comprises the steps: constructing a control network with an industrial computer as an upper computer and controllers of an extruder, an aluminum bar heating furnace and a mold heating furnace as lower computers, designing main control software for the industrial computer, building the communication between the upper computer and the lower computer through a TCP / IP protocol, and carrying out the control of the upper computer and the lower computer; meanwhile, data exchange and real-time information acquisition with the MES system are carried out; process parameters in the aluminum profile extrusion process are obtained from an MES system, real-time data of the process parameters are input into the machine learning model, and the optimized process parameters are output; and according to the process parameters, the preheating temperature of the die and the aluminum bar controlled by energy consumption and the opening time of a die heating furnace door and an aluminum bar heating furnace door are automatically and accurately calculated in the software module. Through combination of an industrial computer, an MES system and a machine learning technology and accurate calculation of equipment control parameters, efficient energy consumption control and production optimization are realized.

Description

Technical Field

[0001] The invention relates to the technical field of energy consumption control in aluminum profile extrusion production, and in particular to an energy consumption control method and system for an aluminum profile extrusion process. Background Art

[0002] The rapid development of the global economy has led to the vigorous development of fields such as construction, industry, aerospace, etc. Due to the good material ductility, easy processing, and easy formability of aluminum profiles, the market demand for them in the fields of construction, industry, aerospace, etc. remains high.

[0003] The extrusion process of aluminum profiles not only requires an aluminum rod heating furnace and a mold heating furnace to preheat the aluminum rod and the mold respectively, but also requires the extruder to use a hydraulic system to make the extrusion rod generate a very large extrusion force to extrude the aluminum rod through the corresponding mold to obtain the required shape of the product. Therefore, the extrusion production of aluminum profiles is a high-energy consumption and high-emission production process. However, at present, the aluminum rod heating furnace, mold heating furnace, extruder and MES system in the extrusion workshop of aluminum profile manufacturers are relatively independent and not integrated into one to perform unified control of the aluminum profile extrusion process, resulting in various process step information cannot be used in the control of each device, and the order data of the MES system cannot be directly used to generate the control parameters of extrusion production. The calculation of parameters such as aluminum rod preheating temperature, mold preheating temperature, and rod length is mostly calculated manually, and the opening time of the aluminum rod heating furnace door and the mold heating furnace door is controlled by experience.

[0004] Therefore, in order to ensure that the aluminum bar temperature and the mold temperature are suitable for the extrusion process requirements, the aluminum bar preheating temperature and the mold preheating temperature are often increased. This not only leads to the aluminum bar preheating temperature and the mold preheating temperature being too high and being stranded outside the equipment, that is, the time from coming out of the heating furnace to being installed on the extruder is too long, that is, too much energy is wasted, and energy conservation and emission reduction in aluminum profile extrusion production are not achieved, but also the labor intensity of workers is increased.

[0005] Therefore, in the context of carbon peak and carbon neutrality, how to improve production efficiency, control production energy consumption and minimize energy consumption through system integration and precise calculation of equipment control parameters is still a technical problem that needs to be solved urgently in aluminum profile extrusion production. Summary of the invention

[0006] The purpose of the present invention is to provide an energy consumption control method and system for an aluminum profile extrusion process, which can improve production efficiency and minimize energy consumption through system integration and accurate calculation of control parameters of each device.

[0007] Based on one main aspect of the present invention, a method for controlling energy consumption in an aluminum profile extrusion process is provided, comprising one or a combination of the following steps:

[0008] Step 1, constructing a control network with an industrial computer as the host computer and controllers of an extruder, an aluminum rod heating furnace, and a mold heating furnace as the slave computers; the control network adopts an industrial Ethernet communication protocol and realizes real-time data transmission and the issuance of control instructions;

[0009] Step 2: Design the main control software for the industrial computer, establish communication between the host computer and the slave device through the TCP / IP protocol, and exchange data and obtain real-time information with the MES system;

[0010] Step 3: Obtain the process parameters of the aluminum profile extrusion process from the MES system, wherein the process parameters include the mold temperature T m , Aluminum rod extrusion temperature T j , aluminum rod length L, extrusion speed v j , Extrusion rod idle stroke S k , Extrusion rod no-load forward speed v kq , Extrusion rod no-load retreat v kh ; Input the real-time data of the process parameters into the machine learning model and output the optimized process parameters;

[0011] Step 4: Test the temperature drop rate △T of the preheated aluminum bar and preheated mold at room temperature lb , △T m ;

[0012] Step 5: Test the time t of taking the mold out of the mold heating furnace and installing it on the extruder mjaz ;

[0013] Step 6: Test the aluminum rod cutting time t cj , the time t for the aluminum bar to be installed on the extruder lbaz ;

[0014] Step 7: Calculate the working time t of one aluminum bar extrusion cycle jxh The working time of one aluminum rod extrusion cycle is t jxh is the extrusion time of a single aluminum bar t jy , Extrusion rod no-load retreat time t kh , Extrusion rod no-load forward time t kq sum;

[0015] Among them, the extrusion time of the single aluminum rod is The no-load retreat time of the extrusion rod

[0016] The time for the extrusion rod to move forward without load

[0017] Step 8: Calculate the time t that the aluminum bar stays outside the equipment lbzl The length of time t that the aluminum rod is retained outside the devicelbzl is the time t that the aluminum rod is pushed out of the heating furnace tc , aluminum bar cutting time t cj , the time t for the aluminum bar to be installed on the extruder lbaz sum;

[0018] The time it takes for the aluminum rod to be pushed out of the heating furnace is Where L0 is the distance from the aluminum bar heating furnace door to the shear knife, v lbtc It is the speed at which the aluminum rod is pushed out of the heating furnace.

[0019] Step 9: Calculate the time t in the extrusion cycle corresponding to when the aluminum bar heating furnace door is opened based on the obtained parameters. lblm and the time t in the extrusion cycle when the mold heating furnace door is opened mjlm ;

[0020] Step 10: Calculate the required preheating temperature T of the aluminum bar according to the preset production requirements and extrusion process parameters. lby And the mold preheating temperature T my ;

[0021] Step 11, controlling the aluminum rod heating furnace and the mold heating furnace according to the calculated parameters, so that the aluminum rod and the mold are preheated to the temperature calculated in step 10, and opening the aluminum rod heating furnace door and the mold heating furnace door at the time point calculated in step 9.

[0022] In the above scheme, efficient energy consumption control and production optimization are achieved through the combination of industrial computers, MES systems and machine learning technology, system integration and precise calculation of equipment control parameters. First, the control network of the host computer (industrial computer) and the slave computer (extruder, aluminum bar heating furnace, mold heating furnace controller) is established by using the industrial Ethernet communication protocol. Compared with traditional Ethernet, industrial Ethernet is more suitable for industrial environments and has high reliability, real-time and anti-interference capabilities. Through industrial Ethernet, seamless communication between devices can be achieved, ensuring the coordinated operation of equipment such as extruders and heating furnaces, reducing production inconsistencies caused by information delays, improving the response speed and accuracy of the production line, and ensuring that each device can respond to operating instructions in the shortest time.

[0023] The main control software is designed in the industrial computer, and the TCP / IP protocol is used to communicate with the lower-level equipment, and at the same time, data is exchanged and real-time information is obtained with the MES system. Through integration with the MES system, the main control software can monitor the production process in all directions and ensure the real-time synchronization of production parameters and order requirements.

[0024] Moreover, through the introduction of machine learning models, the optimization process of process parameters no longer relies on experience, but is based on dynamic analysis of historical data and real-time working conditions. The model can continuously learn and optimize during the production process, improving the level of intelligent production. The model predicts and optimizes production conditions by analyzing energy consumption data during the production process in real time to minimize production energy consumption. Energy waste is reduced by automatically adjusting parameters such as heating temperature and extrusion speed.

[0025] By testing the installation and preheating process of aluminum bars and molds, the time of each operation link is accurately calculated, and the opening time of the heating furnace door is adjusted according to real-time data, so that the energy consumption in the production process is minimized.

[0026] In some embodiments, as a further preferred embodiment, the mold temperature T m , Aluminum rod extrusion temperature T j Real-time monitoring using thermocouple sensors. Thermocouple sensors can maintain stable performance in high temperature environments and provide fast temperature feedback, which helps to adjust the operation of the heating furnace and extruder in time to ensure that the process temperature is always in the optimal range.

[0027] In some embodiments, as a further preferred embodiment, the aluminum rod heating furnace door is opened at the time t in the extrusion cycle. lblm The calculation formula is:

[0028] When mold replacement is not required: t lblm =t jxh -t lbzl ,

[0029] When mold replacement is required: t lblm =t jxh +t mjaz -t lbzl ,

[0030] The mold heating furnace door is opened when the last product of a batch of products is completed, and the opening time is t mjlm =t jxh -t mjaz Specifically, this heating furnace door opening control based on the extrusion cycle and production rhythm greatly improves the continuity and efficiency of the production process and reduces unnecessary energy waste.

[0031] In some embodiments, as a further preferred embodiment, the preheating temperature required for the aluminum rod is The preheating temperature T required for the mold my =T m +△T m ×t mjaz .

[0032] In some embodiments, as a further preferred embodiment, the mold temperature T m , Aluminum rod extrusion temperature T j Real-time monitoring using thermocouple sensors.

[0033] In some embodiments, as a further preferred solution, a timer for the opening time of the aluminum rod heating furnace door is provided in the main control software of the industrial computer;

[0034] The aluminum rod heating furnace door opening time timer starts timing from 0 when the aluminum rod begins to be extruded, that is, when the pressure sensor of the hydraulic cylinder of the extruder detects that the cylinder pressure is greater than the hydraulic cylinder pressure when the extrusion rod moves forward without load.

[0035] In some embodiments, as a further preferred embodiment, when the aluminum rod heating furnace door opening time timer counts to the time when the aluminum rod heating furnace door opens, the main control software sends an instruction to the controller of the aluminum rod heating furnace, the aluminum rod heating furnace door opens, and the aluminum rod is pushed out of the heating furnace, and then cut according to the length of the aluminum rod and installed on the extruder.

[0036] The technical innovation of setting a timer for the opening time of the aluminum bar heating furnace door in the main control software of the industrial computer effectively optimizes the heating control and energy consumption management in the aluminum profile extrusion production process. By triggering the timer according to the real-time data of the hydraulic cylinder pressure sensor of the extruder and accurately controlling the opening time of the heating furnace door, the system can ensure that the aluminum bar enters the extruder under the most suitable temperature conditions to avoid the impact of temperature fluctuations on product quality. This intelligent control not only improves the stability and consistency of the production process, but also significantly reduces energy consumption, providing important technical support for enterprises to achieve efficient and energy-saving production.

[0037] Moreover, after the aluminum rod heating furnace door opening time timer counts to the opening of the aluminum rod heating furnace door, an opening instruction is sent to the aluminum rod heating furnace controller through the main control software, thereby realizing the automated process of smoothly pushing the aluminum rod out of the heating furnace and accurately cutting and installing it into the extruder according to the length of the aluminum rod.

[0038] In some embodiments, as a further preferred scheme, a product counter and a mold heating furnace door opening time timer are set in the main control software of the industrial computer; when the count of the product counter is equal to the number of product batches - 1 and the aluminum rod starts to be extruded again, the mold heating furnace door opening time timer starts timing from 0.

[0039] In some embodiments, as a further preferred embodiment, when the mold heating furnace door opening time timer counts to the time when the mold heating furnace door opens, the main control software sends an instruction to the controller of the mold heating furnace, the mold heating furnace door opens, and the mold is taken out of the heating furnace and installed on the extruder.

[0040] In some embodiments, the product counter can accurately record the number of products in each batch and trigger the mold heating furnace door opening timer when the aluminum bar begins to be extruded, starting from zero. By accurately recording the time when the mold heating furnace door is opened by the timer, the main control software can issue instructions at the best time to ensure that the mold is immediately taken out and installed on the extruder after reaching the predetermined temperature. Controlling the precise opening time of the mold heating furnace door avoids unnecessary loss of energy in the heating furnace.

[0041] In some embodiments, as a further preferred solution, the machine learning model is trained in combination with historical process parameters and production data of the MES system; the trained machine learning model is exported and stored in an industrial computer; a machine learning module is set in the main control software, and the trained model is loaded to ensure that this module can access real-time data of process parameters;

[0042] The machine learning model includes but is not limited to supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms.

[0043] In the above scheme, the machine learning model is trained using historical data in the MES system to learn the impact of different process parameters on energy consumption and production efficiency. Before inputting the data into the machine learning model, data preprocessing is required to ensure the quality and availability of the data, and to normalize or standardize the data features so that different features will not produce deviations due to different dimensions during model training. It is divided into training set, validation set and test set, with 80% of the data used for model training, 10% for validation, and 10% for testing. The training set is used to learn the model, the validation set is used to adjust hyperparameters (such as model depth, learning rate), and the test set is used to evaluate the performance of the model on unseen data.

[0044] In some embodiments, supervised learning algorithms such as random forest or linear regression are used to train data in an industrial computer. Based on historical data, the algorithm learns the relationship between different process parameters and energy consumption, predicts the impact of different process parameters on energy consumption, and finds the optimal combination of production parameters. Among them, random forest is suitable for finding the best combination from multiple process parameters to minimize energy consumption. Linear regression model is used to predict the relationship between specific extrusion process parameters and energy consumption.

[0045] In some embodiments, unsupervised learning algorithms are used to detect equipment anomalies or optimizations, and clustering algorithms are used to classify equipment operating states into different groups to help identify when equipment is in an abnormal state. Reinforcement learning algorithms are used to adjust and optimize production parameters in real time to achieve minimum energy consumption and optimal production efficiency. Through continuous interaction with the environment, the reinforcement learning model can learn the optimal extrusion parameter settings to minimize energy consumption and maximize production efficiency.

[0046] In some embodiments, the optimal production parameter settings are learned through interaction with the environment through a reinforcement learning algorithm to minimize energy consumption and maximize production efficiency.

[0047] In some embodiments, the model is validated by k-fold cross validation (usually 5-fold or 10-fold) to ensure the robustness and generalization ability of the model. The hyperparameters of the model are adjusted using grid search or random search to ensure that the model can make accurate predictions based on the MES system data.

[0048] In some embodiments, the trained machine learning model is exported to a format that can be read by an industrial computer and stored in the industrial computer. A machine learning module is set in the main control software of the industrial computer to ensure that the trained model can be loaded and run. During the production process, the machine learning model obtains real-time process data from the MES system and generates prediction results. The model automatically generates the optimal operating parameters based on the results predicted by the real-time data and transmits these parameters to the MES system.

[0049] Based on another main aspect of the present invention, an energy consumption control system for an aluminum profile extrusion process is provided, which is used to implement the energy consumption control method for an aluminum profile extrusion process, and is characterized by comprising:

[0050] Software module for real-time reading of various process parameters of the MES system and calculation of various control parameters;

[0051] The equipment module mainly includes a hydraulic extruder, a die heating furnace, and an aluminum bar heating furnace, which are used to preheat the die and aluminum bars and extrude the aluminum bars;

[0052] The communication and control module is used for communication and control of the equipment modules, to achieve integration and coordination of various parts of the system, and to achieve precise monitoring and adjustment of the extrusion process through Ethernet communication protocol, TCP / IP protocol and control technology.

[0053] Advantages and beneficial effects of the present invention:

[0054] The equipment and MES system in the aluminum extrusion production workshop are no longer independent entities, but integrated into one, realizing unified automatic control of equipment and automatic transmission of data. In addition, the main control software combines machine learning models to provide real-time decision support and optimization suggestions based on the process parameter data provided by the MES system in real time. Based on historical data and real-time monitoring results, the system can automatically adjust production plans and parameters to make decisions more scientific and efficient.

[0055] Secondly, various process parameters are automatically read from the MES system, and key parameters such as the preheating temperature of the aluminum bar, the preheating temperature of the mold, the extrusion cycle working time, and the aluminum bar retention time are accurately calculated to ensure that each step in the extrusion process is carried out under optimal conditions. These calculations are based on real-time data, taking into account the working status and production rhythm of the equipment, making process parameters such as temperature, time, and speed more accurate and controllable.

[0056] Finally, through the automated process calculation and control system, the need for human intervention is reduced. The system automatically operates according to the calculation results, reducing process deviations and errors caused by human factors, making the production process more stable and reliable. It also enhances the company's advantages and leading position in market competition. This technological innovation has brought the company a broader development space and the driving force for sustained growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0058] Figure 1 It is a flow chart of the energy consumption control method of the aluminum profile extrusion process of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention will be described in detail below so that the purpose, features and advantages of the present invention can be more clearly understood. It should be understood that the following embodiments are not intended to limit the scope of the present invention, but are only intended to illustrate the essential spirit of the technical solution of the present invention.

[0060] In the following description, certain specific details are set forth for the purpose of illustrating the various disclosed embodiments to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the embodiments may be practiced without one or more of these specific details. In other cases, well-known devices, structures, and techniques associated with the present application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0061] References throughout the specification to "one embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0062] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solution of the present invention, but the present invention is not limited to these embodiments.

[0063] like Figure 1 The figure is a flow chart of a method for controlling energy consumption in an aluminum profile extrusion process provided by an embodiment of the present invention.

[0064] An embodiment of the present invention provides a method for controlling energy consumption in an aluminum profile extrusion process, comprising one or a combination of the following steps:

[0065] Step 1, constructing a control network with an industrial computer as the host computer and controllers of an extruder, an aluminum rod heating furnace, and a mold heating furnace as the slave computers; the control network adopts an industrial Ethernet communication protocol and realizes real-time data transmission and the issuance of control instructions;

[0066] Step 2: Design the main control software for the industrial computer, establish communication between the host computer and the slave device through the TCP / IP protocol, and exchange data and obtain real-time information with the MES system;

[0067] Step 3: Obtain the process parameters of the aluminum profile extrusion process from the MES system, wherein the process parameters include the mold temperature T m =420℃, aluminum bar extrusion temperature T j =420℃, aluminum bar length L=810mm, extrusion speed v j =3mm / s, extrusion rod idle stroke S k =300mm, extrusion rod no-load forward speed v kq =30mm / s, extrusion rod no-load retreat v kh =50mm / s; inputting the real-time data of the process parameters into the machine learning model, and outputting the optimized process parameters;

[0068] Step 4: Test the temperature drop rate △T of the preheated aluminum bar and preheated mold at room temperature lb =0.5℃ / s, △T m =0.5℃ / s;

[0069] Step 5: Test the time t of taking the mold out of the mold heating furnace and installing it on the extruder mjaz =15s;

[0070] Step 6: Test the cutting time of aluminum rod cj = 6s, the time it takes for the aluminum bar to be installed on the extruder t lbaz =12s;

[0071] Step 7: Calculate the working time t of one aluminum bar extrusion cycle jxhThe working time of one aluminum rod extrusion cycle is t jxh is the extrusion time of a single aluminum bar t jy , Extrusion rod no-load retreat time t kh , Extrusion rod no-load forward time t kq sum;

[0072] Among them, the extrusion time of the single aluminum rod is The no-load retreat time of the extrusion rod The time for the extrusion rod to move forward without load

[0073] The calculation of the working time of one aluminum bar extrusion cycle t jxh =t jy +t kq +t kh =270+22.2+10=302.2s;

[0074] Step 8: Calculate the time t that the aluminum bar stays outside the equipment lbzl The length of time t that the aluminum rod is retained outside the device lbzl is the time t that the aluminum rod is pushed out of the heating furnace tc , aluminum bar cutting time t cj , the time t for the aluminum bar to be installed on the extruder lbaz sum;

[0075] The distance from the aluminum bar heating furnace door to the shear knife is L0 = 500mm. The speed at which the aluminum bar is pushed out of the heating furnace is v lbtc =40mm / s, where the time it takes for the aluminum rod to be pushed out of the heating furnace

[0076] The length of time t during which the aluminum rod is retained outside the equipment lbzl t lbzl =t tc +t cj +t lbaz =32.75+6+12=50.75s;

[0077] Step 9: Calculate the time t in the extrusion cycle corresponding to when the aluminum bar heating furnace door is opened based on the obtained parameters. lblm and the time t in the extrusion cycle when the mold heating furnace door is opened mjlm ;

[0078] When mold replacement is not required, the aluminum bar heating furnace door is opened at t lblm =t jxh -t lbzl =302.2-50.75=251.45s;

[0079] When the mold needs to be replaced, the aluminum bar heating furnace door is opened at t lblm =t jxh +t mjaz -t lbzl =302.2+15-50.75=266.45s;

[0080] The time when the mold heating furnace door is opened corresponds to the time t in the extrusion cycle mjlm =t jxh -t mjaz =302.2-15=287.2s;

[0081] Step 10: Calculate the required preheating temperature T of the aluminum bar according to the preset production requirements and extrusion process parameters. lby And the mold preheating temperature T my ;

[0082]

[0083] T my =T m +△T m ×t mjaz =420+0.5×15=437.5℃;

[0084] Step 11, controlling the aluminum rod heating furnace and the mold heating furnace according to the calculated parameters, so that the aluminum rod and the mold are preheated to the temperature calculated in step 10, and opening the aluminum rod heating furnace door and the mold heating furnace door at the time point calculated in step 9.

[0085] As a further preferred embodiment, the mold temperature T m , Aluminum rod extrusion temperature T j Real-time monitoring using thermocouple sensors.

[0086] As a further preferred solution, a timer for the aluminum rod heating furnace door opening is set in the main control software of the industrial computer. When the aluminum rod begins to be extruded, that is, when the pressure sensor of the hydraulic cylinder of the extruder detects that the cylinder pressure is greater than the hydraulic cylinder pressure when the extrusion rod moves forward without load, the timer for the aluminum rod heating furnace door opening starts to count from 0. When the time for the aluminum rod heating furnace door to open is reached, the main control software sends a command to the controller of the aluminum rod heating furnace, the aluminum rod heating furnace door opens, the aluminum rod is pushed out of the heating furnace, and then cut according to the length of the aluminum rod and installed on the extruder.

[0087] As a further preferred solution, a product counter and a mold heating furnace door opening time timer are set in the main control software of the industrial computer. When the product counter counts equal to the number of product batches - 1 and the aluminum bar starts to be extruded again, the mold heating furnace door opening time timer starts to count from 0. When the time for the mold heating furnace door to open is reached, the main control software sends a command to the controller of the mold heating furnace, the mold heating furnace door is opened, and the mold is taken out of the heating furnace and installed on the extruder.

[0088] In some embodiments, before the process parameter data is input into the machine learning model, data preprocessing is required to ensure the quality and availability of the data and to normalize or standardize the data features so that different features will not produce deviations due to different dimensions during model training.

[0089] In some embodiments, the machine learning algorithm is trained in combination with historical process parameters and production data of the MES system; the trained machine learning model is exported and stored in an industrial computer; a machine learning module is set in the main control software, and the trained model is loaded to ensure that the module can access the real-time data of the process parameters;

[0090] The machine learning algorithm includes but is not limited to a supervised learning algorithm, an unsupervised learning algorithm, and a reinforcement learning algorithm.

[0091] The machine learning model is trained using historical data from the MES system to learn the impact of different process parameters on energy consumption and production efficiency. It is divided into training set, validation set and test set, with 80% of the data used for model training, 10% for validation, and 10% for testing. The training set is used to learn the model, the validation set is used to adjust hyperparameters (such as model depth, learning rate, etc.), and the test set is used to evaluate the performance of the model on unseen data.

[0092] As a further preferred solution, supervised learning algorithms such as random forest or linear regression are used to train the data in an industrial computer. Based on historical data, the algorithm learns the relationship between different process parameters and energy consumption, predicts the impact of different process parameters on energy consumption, and finds the optimal combination of production parameters. Among them, random forest is suitable for finding the best combination from a variety of process parameters to minimize energy consumption. Linear regression model is used to predict the relationship between specific extrusion process parameters and energy consumption.

[0093] Random forest is an ensemble learning algorithm that improves the accuracy and stability of the model by building multiple decision trees.

[0094] In some embodiments, the training process of the random forest ensemble learning algorithm:

[0095] The Bootstrap method is used to extract different subsets from the training set, and each subset is used to train a decision tree. Each tree divides the data according to specific process parameter selection conditions so that the samples of each leaf node are as similar as possible (divided by minimizing the Gini coefficient or information gain). The final result is determined by a voting mechanism based on the prediction results of each tree. In regression problems, the prediction result is the average of all trees.

[0096] The random forest model learns the impact of process parameters on energy consumption through the voting mechanism of multiple trees and predicts the optimal combination of process parameters to minimize energy consumption.

[0097] For regression problems, the mean square error is used as the loss function to measure the deviation between the predicted value and the true value. The MSE formula is as follows:

[0098] Among them, y i is the actual value, y^ i is the model prediction value, and n is the number of samples.

[0099] The random forest model is able to calculate the importance of each feature and determine which process parameters have the greatest impact on energy consumption by looking at the mean information gain or the decrease in the Gini coefficient when splitting.

[0100] Linear regression is used to analyze the linear relationship between specific process parameters and energy consumption.

[0101] In some embodiments, the training process of the linear regression learning algorithm:

[0102] Linear regression assumes that there is a linear relationship between energy consumption Y and process parameters X. The model form is: Y = + β 0 +β 1 X 1 +β 2 X 2 +...+β p X p +∈

[0103] Among them, β 0 is the intercept, β 2 ,...,β p , is the weight of the process parameters, and ∈ is the error term.

[0104] The goal of the model is to find the best β parameter by minimizing the error, using the least squares method: That is, minimize the sum of squares of actual and predicted values.

[0105] After obtaining the optimal parameters, new process parameters can be input to predict their impact on energy consumption.

[0106] As a further preferred solution, unsupervised learning algorithms are used to detect equipment anomalies or optimizations, and clustering algorithms are used to classify equipment operating states into different groups to help identify when equipment is in an abnormal state. Reinforcement learning algorithms are used to adjust and optimize production parameters in real time to achieve minimum energy consumption and optimal production efficiency. Through continuous interaction with the environment, the reinforcement learning model can learn the optimal extrusion parameter settings to minimize energy consumption and maximize production efficiency.

[0107] K-means clustering is a typical unsupervised learning algorithm used to classify equipment operation status data to identify whether the equipment is in an abnormal state.

[0108] Select K initial centroids (representing different clusters of operating states).

[0109] For each sample, calculate its Euclidean distance to each centroid and assign the sample to the closest cluster: Among them, d(x i ,c k ) is the sample x i to cluster centroid c k distance.

[0110] Update centroids: The new centroid of each cluster is the average of all samples in the cluster.

[0111] Repeat the above process until the centroid no longer changes significantly (i.e., the clustering results converge).

[0112] Through the clustering algorithm, the operating status of the equipment can be divided into normal group and abnormal group, so as to identify whether the equipment is abnormal.

[0113] As a further preferred solution, the optimal production parameter settings are learned through the interaction with the environment through reinforcement learning algorithm to minimize energy consumption and maximize production efficiency.

[0114] State S: current production process parameters; Action A: action to adjust production parameters; Reward R: feedback given by the system after the action is executed; Strategy π: decision-making rules or strategies that determine what actions to take in a certain state.

[0115] Calculation process:

[0116] Q-table initialization: Initialize a Q-value table for each state-action pair.

[0117] Policy update: Based on the current state, choose an action (which can be a greedy choice based on maximizing the current Q-value or a random choice to explore other actions).

[0118] Perform actions and get feedback: After the action is performed, the environment gives a reward (positive or negative) and enters the next state.

[0119] Q value update:

[0120] Among them, α is the learning rate, which is a parameter between 0 and 1. In the early stage of the model, the value of α is 0.5, which can accelerate learning. During the training process, the learning rate decay strategy is used, that is, the learning rate is gradually reduced from 0.1 to 0.01 as the training progresses;

[0121] γ is a discount factor, which is used to determine the importance of future rewards in current decisions. It measures the degree of discount of future rewards and takes a value of 0.9 or 0.95;

[0122] R is the immediate reward, which is the feedback value returned by the environment to the agent after the agent takes a specific action in a certain state. It indicates how "good" or "bad" the action is in the current state. Positive rewards are given to successful behaviors to encourage the agent to take such actions. Negative rewards are given to failed or bad behaviors to inhibit such behaviors.

[0123] Q(s′,a′) is the optimal Q value for the next state.

[0124] Through repeated interactions, reinforcement learning gradually optimizes the Q table and finds the combination of production parameters that minimizes energy consumption and maximizes production efficiency.

[0125] As a further preferred solution, the model is validated through K-fold cross validation (usually 5-fold or 10-fold) to ensure the robustness and generalization ability of the model. K-fold cross validation divides the data into K subsets, and one of the subsets is used as the validation set and the other K-1 subsets are used as the training set. The validation error of the model is calculated K times, and the average is taken as the final error evaluation indicator.

[0126] As a further preferred solution, grid search or random search is used to adjust the model's hyperparameters to ensure that the model can make accurate predictions based on the MES system data. Grid search searches for the optimal combination in a predefined hyperparameter grid. Random search randomly samples several times in the hyperparameter space to find the best hyperparameter combination.

[0127] Export the trained machine learning model to a format that can be read by the industrial computer and store it in the industrial computer. Set up the machine learning module in the main control software of the industrial computer to ensure that the trained model can be loaded and run. During the production process, the machine learning model obtains real-time process data from the MES system and generates prediction results. The model automatically generates the optimal operating parameters based on the results predicted by the real-time data and transmits these parameters to the MES system.

[0128] As a further preferred solution, at each stage of the extrusion process, the current process parameters are collected in real time and input into the model to obtain the predicted optimal heating temperature and extrusion time of the aluminum bar and the mold. And according to the model prediction results, the temperature setting values ​​of the aluminum bar heating furnace and the mold heating furnace are dynamically adjusted. When the aluminum bar begins to be extruded, the extrusion speed is adjusted according to the real-time temperature and pressure information to ensure product quality.

[0129] When the model predicts that the set aluminum rod heating temperature is about to be reached, the main control software sends a command to the lower computer device through the TCP / IP protocol to open the aluminum rod heating furnace door.

[0130] Test the real-time prediction capability of the model in the actual production environment and monitor the deviation between the model output and the actual production efficiency. Continuously adjust the model parameters and control strategies based on real-time monitoring data to optimize the heating time and temperature of aluminum bars and molds.

[0131] By integrating machine learning algorithms into industrial computer control software, smarter control of the aluminum extrusion process can be achieved.

[0132] Based on the second main aspect of the present invention, an energy consumption control system for an aluminum profile extrusion process is provided, which is used to implement the energy consumption control method for an aluminum profile extrusion process, comprising:

[0133] Software module for real-time reading of various process parameters of the MES system and calculation of various control parameters;

[0134] The equipment module mainly includes a hydraulic extruder, a die heating furnace, and an aluminum bar heating furnace, which are used to preheat the die and aluminum bars and extrude the aluminum bars;

[0135] The communication and control module is used for communication and control of the equipment modules, to achieve integration and coordination of various parts of the system, and to achieve precise monitoring and adjustment of the extrusion process through Ethernet communication protocol, TCP / IP protocol and control technology.

[0136] The matters not described in detail in the present invention are all known technologies to those skilled in the art.

[0137] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for controlling energy consumption in an aluminum profile extrusion process, characterized in that: The method includes one or a combination of the following steps: Step 1, constructing a control network with an industrial computer as the host computer and controllers of an extruder, an aluminum rod heating furnace, and a mold heating furnace as the slave computers; the control network adopts an industrial Ethernet communication protocol to realize real-time data transmission and the issuance of control instructions; Step 2: Design the main control software for the industrial computer, establish communication between the host computer and the slave device through the TCP / IP protocol, and exchange data and obtain real-time information with the MES system; Step 3: Obtain the process parameters of the aluminum profile extrusion process from the MES system, wherein the process parameters include the mold temperature T m , Aluminum rod extrusion temperature T j , aluminum rod length L, extrusion speed v j , Extrusion rod idle stroke S k , Extrusion rod no-load forward speed v kq , Extrusion rod no-load retreat v kh ; Input the real-time data of the process parameters into the machine learning model and output the optimized process parameters; Step 4: Test the temperature drop rate △T of the preheated aluminum bar and preheated mold at room temperature lb , △T m ; Step 5: Test the time t of taking the mold out of the mold heating furnace and installing it on the extruder mjaz ; Step 6: Test the aluminum rod cutting time t cj , the time t for the aluminum bar to be installed on the extruder lbaz ; Step 7: Calculate the working time t of one aluminum bar extrusion cycle jxh The working time of one aluminum rod extrusion cycle is t jxh is the extrusion time of a single aluminum bar t jy , Extrusion rod no-load retreat time t kh , Extrusion rod no-load forward time t kq sum; Among them, the extrusion time of the single aluminum rod is The no-load retreat time of the extrusion rod The time for the extrusion rod to move forward without load Step 8: Calculate the time t that the aluminum bar stays outside the equipment lbzl The length of time t that the aluminum rod is retained outside the device lbzl is the time t that the aluminum rod is pushed out of the heating furnace tc , aluminum bar cutting time t cj , the time t for the aluminum bar to be installed on the extruder lbaz sum; The time it takes for the aluminum rod to be pushed out of the heating furnace is Where L0 is the distance from the aluminum bar heating furnace door to the shear knife, v lbtc The speed at which the aluminum rod is pushed out of the heating furnace; Step 9: Calculate the time t in the extrusion cycle corresponding to when the aluminum bar heating furnace door is opened based on the obtained parameters. lblm and the time t in the extrusion cycle when the mold heating furnace door is opened mjlm ; Step 10: Calculate the required preheating temperature T of the aluminum bar according to the preset production requirements and extrusion process parameters. lby And the mold preheating temperature T my ; Step 11, controlling the aluminum rod heating furnace and the mold heating furnace according to the calculated parameters, so that the aluminum rod and the mold are preheated to the temperature calculated in step 10, and opening the aluminum rod heating furnace door and the mold heating furnace door at the time point calculated in step 9.

2. The method for controlling energy consumption during aluminum profile extrusion process according to claim 1, characterized in that: The mold temperature T m , Aluminum rod extrusion temperature T j Real-time monitoring using thermocouple sensors.

3. The method for controlling energy consumption during aluminum profile extrusion process according to claim 1, characterized in that: The aluminum rod heating furnace door is opened at the time t in the extrusion cycle. lblm The calculation formula is: When mold replacement is not required: t lblm =t jxh -t lbzl , When mold replacement is required: t lblm =t jxh +t mjaz -t lbzl , The mold heating furnace door is opened when the last product of a batch of products is completed, and the opening time is t mjlm =t jxh -t mjaz .

4. The method for controlling energy consumption during aluminum profile extrusion process according to claim 1, characterized in that: The preheating temperature required for the aluminum rod The preheating temperature T required for the mold my =T m +△T m ×t mjaz .

5. The method for controlling energy consumption during aluminum profile extrusion process according to claim 1, characterized in that: The main control software of the industrial computer is provided with a timer for the opening time of the aluminum rod heating furnace door; The aluminum rod heating furnace door opening time timer starts timing from 0 when the aluminum rod begins to be extruded, that is, when the pressure sensor of the hydraulic cylinder of the extruder detects that the cylinder pressure is greater than the hydraulic cylinder pressure when the extrusion rod moves forward without load.

6. The method for controlling energy consumption during aluminum profile extrusion process according to claim 5, characterized in that: When the aluminum rod heating furnace door opening time timer counts to the time when the aluminum rod heating furnace door opens, the main control software sends a command to the controller of the aluminum rod heating furnace, the aluminum rod heating furnace door opens, the aluminum rod is pushed out of the heating furnace, and then cut according to the length of the aluminum rod and installed on the extruder.

7. The method for controlling energy consumption during aluminum profile extrusion process according to claim 1, characterized in that: A product counter and a mold heating furnace door opening time timer are set in the main control software of the industrial computer; when the count of the product counter is equal to the number of product batches-1 and the aluminum rod begins to be extruded again, the mold heating furnace door opening time timer starts timing from 0.

8. The method for controlling energy consumption during aluminum profile extrusion process according to claim 7, characterized in that: When the mold heating furnace door opening time timer counts to the time when the mold heating furnace door opens, the main control software sends a command to the controller of the mold heating furnace, the mold heating furnace door opens, and the mold is taken out of the heating furnace and installed on the extruder.

9. The method for controlling energy consumption during aluminum profile extrusion process according to claim 1, characterized in that: The machine learning model is trained in combination with the historical process parameters and production data of the MES system; the trained machine learning model is exported and stored in an industrial computer; a machine learning module is set in the main control software, and the trained model is loaded to ensure that the module can access the real-time data of the process parameters; The machine learning model includes but is not limited to supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms.

10. An energy consumption control system for an aluminum profile extrusion process, used to implement the energy consumption control method for an aluminum profile extrusion process as claimed in any one of claims 1 to 8, characterized in that: include: Software module for real-time reading of various process parameters of the MES system and calculation of various control parameters; The equipment module mainly includes a hydraulic extruder, a die heating furnace, and an aluminum bar heating furnace, which are used to preheat the die and aluminum bars and extrude the aluminum bars; The communication and control module is used for communication and control of the equipment modules, realizing the integration and coordination of various parts of the system, and realizing accurate monitoring and adjustment of the extrusion process through industrial Ethernet communication protocol, TCP / IP protocol and control technology.

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