An intelligent temperature control method for aluminum alloy production based on the Internet of Things

Abstract

The present invention relates to the technical field of temperature control in aluminum alloy production through computer-aided design. Specifically, it relates to an intelligent temperature control method for aluminum alloy production based on the Internet of Things. The steps of this method include: obtaining aluminum alloy production operation data, extracting characteristic parameters of the aluminum alloy production operation data, where the characteristic parameters include: furnace temperature, energy consumption, and product quality score, and performing standardization processing on the aluminum alloy production operation data; constructing an aluminum alloy production temperature control model, where the aluminum alloy production temperature control model defines maximizing product quality and energy efficiency as the optimization objective, and at the same time defines various constraint conditions of the aluminum alloy production temperature control model, inputting the characteristic parameters of the aluminum alloy production operation data into the aluminum alloy production temperature control model for calculation, solving the optimal adjustment strategy for the aluminum alloy production temperature, and completing the precise control of the aluminum alloy production temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control in aluminum alloy production through computer-aided design. Specifically, it relates to an intelligent temperature control method for aluminum alloy production based on the Internet of Things. Background Art

[0002] Aluminum alloy production is a complex industrial process, in which temperature control is crucial for product quality and energy efficiency. At present, the traditional temperature control technology for aluminum alloy production has limitations in many aspects. On the one hand, the strategy of implementing temperature control based on the experience of staff is difficult to adapt to the complex and changeable production environment, which easily leads to insufficient control accuracy and product quality fluctuations. On the other hand, due to the lack of real-time monitoring and optimization ability of energy consumption, the energy use strategy cannot be dynamically adjusted according to production requirements, which not only increases production costs, but also reduces the environmental performance and sustainable development ability of enterprises. In the context of fluctuating energy prices and increasingly strict environmental requirements, this problem has become particularly prominent. Based on this, in view of the above problems, we have designed an intelligent temperature control method for aluminum alloy production based on the Internet of Things. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent temperature control method for aluminum alloy production based on the Internet of Things. By constructing a data-driven temperature control model and aiming at the dual optimization of product quality and energy efficiency, it not only realizes precise temperature control in the aluminum alloy production process, but also can adapt to complex production environments, thereby improving product quality and energy efficiency.

[0004] The embodiments of the present invention are realized through the following technical solutions:

[0005] An intelligent temperature control method for aluminum alloy production based on the Internet of Things, the steps of the method include:

[0006] Obtain the operation data of aluminum alloy production, extract the characteristic parameters of the operation data of aluminum alloy production, the characteristic parameters include: furnace temperature, energy consumption and product quality score, and perform standardization processing on the operation data of aluminum alloy production;

[0007] Construct an aluminum alloy production temperature control model, the aluminum alloy production temperature control model is defined with the maximization of product quality and energy efficiency as the optimization goal, and at the same time define various constraint conditions of the aluminum alloy production temperature control model, input the characteristic parameters of the operation data of aluminum alloy production into the aluminum alloy production temperature control model for calculation, and solve the optimal adjustment strategy of the aluminum alloy production temperature to complete the precise control of the aluminum alloy production temperature.

[0008] Optionally, the formula for extracting the characteristic parameters of the operation data of aluminum alloy production is:

[0009] Tt = f T (t)

[0010]

[0011] Q t = w1C t + w2S t + w3M t

[0012] where, T t is the furnace temperature at time t, f T (t) is the temperature measurement function, E t is the cumulative energy consumption at time t, P(τ) is the power function, Δt is the statistical time interval, Q t is the product quality score at time t, C t , S t , M t are the scores of composition, strength and microstructure respectively, and w1, w2, w3 are the weight coefficients.

[0013] Optionally, by calculating through the energy consumption and the product quality score, an energy efficiency index is obtained, and its calculation formula is:

[0014]

[0015] where, EEI t is the energy efficiency index at time t.

[0016] Optionally, the aluminum alloy production temperature control model is constructed, which specifically includes: an input layer, a hidden layer and an output layer; where, the calculation formula of the input layer is:

[0017] a (1) = [T t , E t , Q t , EEI t

[0018] where, a (1) is the activation value of the input layer;

[0019] The calculation formula of the hidden layer is:

[0020] z (l) = W (l) a (l-1) + b (l)

[0021] a (l) = g(z (l) )

[0022] where, z​(l) is the linear combination output of the l-th layer, W (l) is the weight matrix of the l-th layer, b (l) is the bias of the l-th layer, a (l) is the activation value of the l-th layer, and g() is the activation function;

[0023] The calculation formula of the output layer is:

[0024] ΔT = W (L) a (L-1) + b (L)

[0025] where ΔT is the temperature adjustment amount, representing the optimal adjustment strategy of the aluminum alloy production temperature, W (L) is the weight matrix of the output layer, b (L) is the bias of the output layer, a (L-1) is the activation value of the last hidden layer, and L is the total number of layers of the network.

[0026] Optionally, the aluminum alloy production temperature control model is defined with the maximization of product quality and energy efficiency as the optimization goal, and its calculation formula is:

[0027]

[0028] where maxJ is the objective function, N is the cycle length, Q base is the reference product quality score, EEI ref is the reference energy efficiency level, ΔQ t = Q t - Q t-1 is the quality change at adjacent times, ΔEEI t = EEI t - EEI t-1 is the energy efficiency change at adjacent times, is the average product quality, is the average energy efficiency, E target is the target energy consumption level, and k is the energy consumption sensitivity parameter.

[0029] Optionally, the various constraint conditions for defining the aluminum alloy production temperature control model include: temperature constraint, temperature change rate constraint, energy consumption constraint, instantaneous energy consumption constraint, energy efficiency constraint, product quality constraint, quality stability constraint, comprehensive performance lower limit constraint, energy efficiency improvement constraint, and quality and energy efficiency balance constraint.

[0030] Optionally, the calculation formula of the temperature constraint is:

[0031] T min ≤ T t ≤ T max

[0032] The calculation formula for the temperature change rate constraint is:

[0033] |ΔT t | = |T t - T t-1 | ≤ ΔT max

[0034] The calculation formula for the energy consumption constraint is:

[0035]

[0036] The calculation formula for the instantaneous energy consumption constraint is:

[0037] E t ≤ E max

[0038] The calculation formula for the energy efficiency constraint is:

[0039] EEI t ≥ EEI min

[0040] The calculation formula for the product quality constraint is:

[0041] Q t ≥ Q min

[0042] The calculation formula for the quality stability constraint is:

[0043] |ΔQ t | = |Q t - Q t-1 | ≤ ΔQ max

[0044] The calculation formula for the comprehensive performance lower limit constraint is:

[0045] J ≥ J min

[0046] The calculation formula for the energy efficiency improvement constraint is:

[0047]

[0048] The calculation formula for the quality - energy efficiency balance constraint is:

[0049]

[0050] Among them, T min 、T max are the lower and upper limits of temperature respectively, ΔT t is the temperature difference between the t - th moment and the (t - 1)-th moment, ΔT max is the maximum temperature change rate, Et The energy consumption at time t, E target The average energy consumption level, E max The maximum instantaneous energy consumption, EEI min The minimum energy efficiency requirement, Q t The product quality score at time t, Q min The minimum product quality standard, ΔQ t The difference in quality scores between time t and time t - 1, ΔQ max The maximum quality change range, J min The minimum comprehensive performance index, EEI target The average energy efficiency level, ∈ is the maximum imbalance degree.

[0051] Optionally, the standardization processing of the aluminum alloy production operation data has the following calculation formula:

[0052]

[0053] where, x norm is the standardized eigenvalue, x is the original eigenvalue, M is the total number of samples, x i is the eigenvalue of the i-th sample.

[0054] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0055] By constructing a data-driven temperature control model and aiming at the dual optimization of product quality and energy efficiency, the embodiment of the present invention not only realizes precise temperature control in the aluminum alloy production process, but also can adapt to complex production environments, thereby improving product quality and energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a schematic flow chart of an intelligent temperature control method for aluminum alloy production based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0058] As Figure 1 shown, Figure 1 is a schematic flow chart of an intelligent temperature control method for aluminum alloy production based on the Internet of Things provided by an embodiment of the present invention.

[0059] In one embodiment, an intelligent temperature control method for aluminum alloy production based on the Internet of Things, the steps of the method include:

[0060] Obtain the aluminum alloy production operation data, extract the characteristic parameters of the aluminum alloy production operation data, the characteristic parameters include: furnace temperature, energy consumption and product quality score, and perform standardization processing on the aluminum alloy production operation data;

[0061] Construct an aluminum alloy production temperature control model, the aluminum alloy production temperature control model is defined with the maximization of product quality and energy efficiency as the optimization goal, and at the same time define various constraint conditions of the aluminum alloy production temperature control model, input the characteristic parameters of the aluminum alloy production operation data into the aluminum alloy production temperature control model for calculation, solve the optimal adjustment strategy of the aluminum alloy production temperature, and complete the precise control of the aluminum alloy production temperature.

[0062] In implementation, this embodiment of the aluminum alloy production temperature control is mainly specifically designed based on the melting and casting process of aluminum alloy, and its specific data collection is carried out through the Internet of Things. Among them, for the furnace temperature, temperature sensors are mainly used, which are respectively arranged inside and outside the furnace wall of the furnace. Energy consumption mainly uses power monitoring equipment and / or gas flow meters, etc. The power monitoring equipment mainly includes current and voltage sensors, etc., which are mainly installed in the power supply area of the furnace to measure the power consumption. The gas flow meter is mainly installed at the gas supply pipeline. It can be understood that the product in this embodiment is characterized as the finished product of aluminum alloy, and its quality score mainly depends on the evaluation of the composition, strength and microstructure of the aluminum alloy finished product. The test results of each aspect will be given a certain weight according to their importance, and finally a comprehensive quality score is formed.

[0063] Specifically, for the extraction of the characteristic parameters of the aluminum alloy production operation data, among them, the calculation formula for the furnace temperature is:

[0064] T t =f T (t)

[0065] The calculation formula for energy consumption is:

[0066]

[0067] The calculation formula for the product quality score is:

[0068] Q t =w1C t +w2S t +w3M t

[0069] Where, T t is the furnace temperature at time t, f T(t) is the temperature measurement function, which is discrete data points directly obtained from a temperature sensor in this embodiment, E t is the cumulative energy consumption at time t, P(τ) is the power function, which is obtained by a power monitoring device in this embodiment, Δt is the statistical time interval, Q t is the product quality score at time t, C t , S t , M t are the scores of composition, strength, and microstructure respectively, and w1, w2, w3 are weight coefficients.

[0070] In practical applications, the composition directly affects the performance and workability of aluminum alloys; the strength (including but not limited to tensile strength, hardness, etc.) is related to the service performance and safety of aluminum alloys; the microstructure affects the comprehensive performance of aluminum alloys. Therefore, in this embodiment, the weight coefficient w1 is given as 0.45; w2 is 0.35; and the microstructure is 0.2. Assuming the scores of each item of a batch of aluminum alloy products are as follows: composition score: 90 points; strength score: 85 points; microstructure score: 80 points; then the final quality score is: 90 * 45% + 85 * 35% + 80 * 20% = 86.75. It can not only comprehensively reflect the core quality characteristics of aluminum alloy products, but also facilitate rapid evaluation and quality control during the production process. It can be understood that the specific weights can be appropriately adjusted according to actual requirements and application scenarios.

[0071] Furthermore, by calculating through the energy consumption and product quality scores, an energy efficiency index is obtained, and its calculation formula is:

[0072]

[0073] where, EEI t is the energy efficiency index at time t.

[0074] In implementation, introducing the energy efficiency index in this embodiment can make the subsequent construction of the aluminum alloy production temperature control model more sensitive to the balance between quality and energy consumption, and help find a better balance point between the two.

[0075] In the specific application of this embodiment, for the constructed aluminum alloy production temperature control model, the aluminum alloy production temperature control model in this embodiment is mainly an MLP (Multi-Layer Perceptron) model, which specifically includes: an input layer, a hidden layer, and an output layer. It can be understood that the aluminum alloy production temperature control model is mainly trained based on historical data, which will not be elaborated in this embodiment.

[0076] Among them, the calculation formula of the input layer is:

[0077] a (1) = [T t , Et , Q t , EEI t

[0078] Among them, a (1) is the activation value of the input layer;

[0079] The calculation formula of the hidden layer is:

[0080] z (l) = W (l) a (l-1) + b (l)

[0081] a (l) = g(z (l) )

[0082] Among them, z (l) is the linear combination output of the l-th layer, W (l) is the weight matrix of the l-th layer, b (l) is the bias of the l-th layer, a (l) is the activation value of the l-th layer, and g() is the activation function;

[0083] The calculation formula of the output layer is:

[0084] ΔT = W (L) a (L-1) + b (L)

[0085] Among them, ΔT is the temperature adjustment amount, representing the optimal adjustment strategy for the aluminum alloy production temperature, W (L) is the weight matrix of the output layer, b (L) is the bias of the output layer, a (L-1) is the activation value of the last hidden layer, and L is the total number of layers of the network.

[0086] The aluminum alloy production temperature control model is defined with the maximization of product quality and energy efficiency as the optimization goal, and its calculation formula is:

[0087]

[0088] Among them, maxJ is the objective function, N is the cycle length, Q base is the reference product quality score, EEI ref is the reference energy efficiency level, ΔQ t = Q t - Q t-1 is the quality change at adjacent times, ΔEEI t = EEI t - EEI t-1 is the energy efficiency change at adjacent times, is the average product quality,​ is the average energy efficiency, E target is the target energy consumption level, and k is the energy consumption sensitivity parameter.

[0089] More specifically, the various constraints defining the aluminum alloy production temperature control model include: temperature constraint, temperature change rate constraint, energy consumption constraint, instantaneous energy consumption constraint, energy efficiency constraint, product quality constraint, quality stability constraint, comprehensive performance lower limit constraint, energy efficiency improvement constraint, and quality and energy efficiency balance constraint.

[0090] The calculation formula for the temperature constraint is:

[0091] T min ≤T t ≤T max

[0092] The calculation formula for the temperature change rate constraint is:

[0093] |ΔT t |=|T t -T t-1 |≤ΔT max

[0094] The calculation formula for the energy consumption constraint is:

[0095]

[0096] The calculation formula for the instantaneous energy consumption constraint is:

[0097] E t ≤E max

[0098] The calculation formula for the energy efficiency constraint is:

[0099] EEI t ≥EEI min

[0100] The calculation formula for the product quality constraint is:

[0101] Q t ≥Q min

[0102] The calculation formula for the quality stability constraint is:

[0103] |ΔQ t |=|Q t -Q t-1 |≤ΔQ max

[0104] The calculation formula for the comprehensive performance lower limit constraint is:

[0105] J ≥ J min

[0106] The calculation formula for the energy efficiency improvement constraint is as follows:

[0107]

[0108] The calculation formula for the mass and energy efficiency balance constraint is as follows:

[0109]

[0110] Among them, T min , T max are the lower and upper temperature limits respectively, ΔT t is the temperature difference between the t-th moment and the (t - 1)-th moment, ΔT max is the maximum temperature change rate, E t is the energy consumption at the t-th moment, E target is the average energy consumption level, E max is the maximum instantaneous energy consumption, EEI min is the minimum energy efficiency requirement, Q t is the product quality score at the t-th moment, Q min is the minimum product quality standard, ΔQ t is the difference in quality scores between the t-th moment and the (t - 1)-th moment, ΔQ max is the maximum mass change amplitude, J min is the minimum comprehensive performance index, EEI target is the average energy efficiency level, and ∈ is the maximum imbalance degree.

[0111] In implementation, the constraint conditions set in this embodiment cover temperature control, energy consumption, product quality, energy efficiency, and the balance relationships among them to jointly ensure the stability, efficiency, and quality of the aluminum alloy production process.

[0112] Furthermore, the calculation formula for standardizing the aluminum alloy production operation data is as follows:

[0113]

[0114] Among them, x norm is the standardized eigenvalue, x is the original eigenvalue, M is the total number of samples, and x i is the eigenvalue of the i-th sample.

[0115] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent temperature control method for aluminum alloy production based on the Internet of Things, characterized in that: The steps of the method include: Acquire aluminum alloy production and operation data, extract characteristic parameters of the aluminum alloy production and operation data, the characteristic parameters include: furnace temperature, energy consumption and product quality score, and perform standardization on the aluminum alloy production and operation data; Constructing an aluminum alloy production temperature control model, wherein the aluminum alloy production temperature control model defines maximizing product quality and energy efficiency as optimization goals, and defines multiple constraints of the aluminum alloy production temperature control model, inputs characteristic parameters of aluminum alloy production operation data into the aluminum alloy production temperature control model for calculation, solves the optimal adjustment strategy for aluminum alloy production temperature, and completes precise control of aluminum alloy production temperature; The energy efficiency index is calculated by the energy consumption and product quality score, and the calculation formula is: in, is the energy efficiency index at time t, Score the product quality at time t, is the cumulative energy consumption at time t; The aluminum alloy production temperature control model is defined to maximize product quality and energy efficiency as the optimization goal, and its calculation formula is: in, is the objective function, is the cycle length, To rate the quality of the benchmark products, For the reference energy efficiency level, is the mass change at adjacent moments, is the change of energy efficiency at adjacent moments, is the average product quality, is the average energy efficiency, is the target energy consumption level, is the energy consumption sensitivity parameter.

2. The intelligent temperature control method for aluminum alloy production based on the Internet of Things according to claim 1 is characterized in that: The calculation formula for extracting the characteristic parameters of the aluminum alloy production operation data is: in, is the furnace temperature at time t, is the temperature measurement function, is the cumulative energy consumption at time t, is the power function, Δt is the statistical time interval, Score the product quality at time t, , , are the scores for composition, strength and microstructure, , , is the weight coefficient.

3. The intelligent temperature control method for aluminum alloy production based on the Internet of Things according to claim 2 is characterized in that: The aluminum alloy production temperature control model is constructed, which specifically includes: an input layer, a hidden layer and an output layer; wherein the calculation formula of the input layer is: in, is the activation value of the input layer; The calculation formula of the hidden layer is: in, For the The linear combination output of the layer, For the The weight matrix of the layer, For the The bias of the layer, For the The activation value of the layer, is the activation function; The calculation formula of the output layer is: in, is the temperature adjustment value, which characterizes the optimal temperature adjustment strategy for aluminum alloy production. is the weight matrix of the output layer, is the bias of the output layer, is the activation value of the last hidden layer, is the total number of layers in the network.

4. The intelligent temperature control method for aluminum alloy production based on the Internet of Things according to claim 3 is characterized in that: The various constraints defining the aluminum alloy production temperature control model include: temperature constraints, temperature change rate constraints, energy consumption constraints, instantaneous energy consumption constraints, energy efficiency constraints, product quality constraints, quality stability constraints, comprehensive performance lower limit constraints, energy efficiency improvement constraints and quality and energy efficiency balance constraints.

5. The intelligent temperature control method for aluminum alloy production based on Internet of Things according to claim 4 is characterized in that: The calculation formula of the temperature constraint is: The calculation formula for the temperature change rate constraint is: The calculation formula for energy consumption constraint is: The calculation formula for the instantaneous energy consumption constraint is: The calculation formula of energy efficiency constraint is: The calculation formula for product quality constraints is: The calculation formula for the quality stability constraint is: The calculation formula for the lower limit constraint of comprehensive performance is: The calculation formula for energy efficiency improvement constraint is: The calculation formula for the quality and energy efficiency balance constraint is: in, , are the lower and upper temperature limits, respectively. is the temperature difference between time t and time t-1, is the maximum temperature change rate, is the energy consumption at time t, is the average energy consumption level, is the maximum instantaneous energy consumption, For minimum energy efficiency requirements, Score the product quality at time t, The minimum product quality standard is is the quality score difference between time t and time t-1, is the maximum mass change, is the minimum comprehensive performance indicator, is the average energy efficiency level, is the maximum imbalance.

6. The method for intelligent temperature control of aluminum alloy production based on the Internet of Things according to any one of claims 1 to 5, characterized in that: The calculation formula for the standardized processing of aluminum alloy production operation data is as follows: in, is the standardized eigenvalue, is the original eigenvalue, is the total number of samples, is the eigenvalue of the i-th sample.

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