A control system for an electric heating heat treatment trolley furnace

Abstract

The present invention relates to the technical field of heat treatment equipment, and discloses a control system for an electric heating heat treatment trolley furnace, comprising a temperature detection module, a data acquisition unit, a data processing unit, a data analysis unit, and a data control unit. By detecting the temperature data in the trolley furnace in real time and establishing a relationship model between the temperature data and the power data, it is possible to accurately adjust the power of the trolley furnace, thereby accurately controlling the temperature in the furnace to meet the strict temperature requirements of the production process; accurate temperature control is helpful to optimize the heat treatment process, reduce production delays and product quality problems caused by temperature deviation, and thus improve production efficiency; through analysis and processing of temperature characteristics, temperature abnormalities of the trolley furnace can be discovered in time, abnormal signals can be generated and displayed to relevant personnel, so that timely measures can be taken to solve the problem and avoid production accidents and product quality losses.

Description

Technical Field

[0001] The invention relates to the technical field of heat treatment equipment, and in particular to a control system of an electric heating heat treatment trolley furnace. Background Art

[0002] In modern industrial production, electric heating heat treatment trolley furnaces are widely used in the heat treatment process of various materials. However, the traditional trolley furnace control system has many shortcomings in temperature control and is difficult to meet the increasing production quality and efficiency requirements.

[0003] With the development of industrial production on a large scale and in a refined manner, precise control of the temperature in the trolley furnace has become crucial. Traditional control systems usually use simple open-loop control or rough adjustment methods based on experience, which cannot accurately and precisely adjust the power according to the actual temperature changes in the trolley furnace in real time, which can easily lead to large temperature fluctuations and affect the heat treatment quality of the product. Moreover, the inability to accurately control the power may not only cause energy waste, but also increase production costs.

[0004] In addition, in the production process, timely detection of abnormal temperature of the trolley furnace is also important for ensuring production safety and product quality. However, the traditional system lacks an effective temperature data monitoring and analysis mechanism, making it difficult to accurately determine whether the temperature is abnormal, and unable to issue an alarm and take corresponding measures in time, which may cause production accidents and bring economic losses to the company.

[0005] In order to solve the above problems, improve the accuracy, stability and energy saving of the temperature control of the trolley furnace, and be able to detect temperature anomalies in time to ensure safe and reliable production, it is imperative to develop an advanced electric heating heat treatment trolley furnace control system. Summary of the invention

[0006] The purpose of the present invention is to provide a control system for an electrically heated heat treatment trolley furnace, which solves the technical problems raised in the background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An electric heating heat treatment trolley furnace control system, comprising:

[0009] The temperature detection module is used to detect the temperature data in the trolley furnace in real time and transmit the temperature data to the data processing module;

[0010] A data acquisition unit is used to obtain the operating data of the trolley furnace, and the operating data includes temperature data under different power data;

[0011] A data processing unit, used for preprocessing the operating data and obtaining a relationship model between the temperature data and the power data according to the preprocessing result;

[0012] The data analysis unit is used to perform control analysis based on the relationship model obtained by the data processing unit, and then determine the current power setting value of the trolley furnace:

[0013] The data control unit is used to reset the power of the trolley furnace according to the power setting value obtained by the data analysis unit.

[0014] As a further solution of the present invention: the pretreatment method is as follows:

[0015] StepM1. Get data for a specific period of time

[0016] Obtain real-time temperature and power data within a specific period of time;

[0017] Step M2: Mark the collected node data

[0018] The specified period is divided into several collection nodes, and the temperature data and power data on each collection node are extracted and marked as Te and Pe respectively, where e=1, 2, ...k, and k represents the number of collection nodes in a specific period;

[0019] Step M3: Build relationship model and determine coefficient

[0020] Constructing the relationship model between temperature data and power data through linear regression algorithm ;

[0021] Among them, a and b are the relationship coefficients to be confirmed;

[0022] The relationship coefficients a and b are determined as follows:

[0023] pass: and , determine the specific values ​​of the relationship coefficients a and b;

[0024] Where Ph and Th are the average values ​​of all Pe and Te, respectively.

[0025] As a further solution of the present invention: the control analysis method is as follows:

[0026] Step K1, obtain the current temperature, current power, and target temperature of the trolley furnace, and mark them as T1, P1, and T0 respectively;

[0027] Step K2, calculate the current temperature deviation value T2 of the trolley furnace through T2=T0-T1;

[0028] Step K3, according to the relationship model obtained by the data processing unit, reverse it, and then combine the current temperature deviation value of the trolley furnace to calculate the power adjustment amount P2 required to reach the target temperature;

[0029] Step K4, calculate the current power setting value Ps of the trolley furnace through Ps=P1+P2.

[0030] As a further solution of the present invention: the calculation formula of the power adjustment amount required to achieve the target temperature is: .

[0031] As a further solution of the present invention: it also includes a data display unit;

[0032] The data processing module is used to receive the temperature data from the temperature detection module, analyze and process it, and extract the corresponding temperature characteristics; the temperature characteristics include the temperature change rate, temperature amplitude rate and temperature deviation rate corresponding to the temperature data in the trolley furnace;

[0033] The data analysis unit is used to perform control analysis of the trolley furnace according to the results obtained by the data processing module, determine whether the trolley furnace is abnormal, and generate an abnormal signal according to the abnormal state of the trolley furnace;

[0034] The data display unit is used to display the abnormal signal to relevant personnel.

[0035] As a further solution of the present invention: the analysis and processing method is as follows:

[0036] StepA1, obtain the real-time temperature data within the specified period;

[0037] StepA2, divide the specified period into several time nodes, extract the temperature data at each time node, and mark it as Ti, i=1, 2, ... n, n represents the number of time nodes in the specified period;

[0038] Step A3, using the mean filtering algorithm, filtering the temperature data at each time node within the specified period, and obtaining the filtered temperature value;

[0039] StepA4, through , calculate the temperature change rate TX within the specified period;

[0040] Step A5: Extract the temperature data with the largest and smallest values ​​from the temperature data at each time node within the specified period and mark them as T max and T min ;

[0041] StepA6. Pass , calculate the temperature amplitude rate TB within the specified period;

[0042] StepA7, obtain the target temperature TM set for the trolley furnace;

[0043] StepA8, through , the temperature deviation rate TC is calculated.

[0044] As a further solution of the present invention: the filtering processing method is as follows:

[0045] pass , calculate and obtain the filtered temperature value TL.

[0046] As a further solution of the present invention: the control analysis method is as follows:

[0047] Step B1. Obtain temperature characteristic data

[0048] Obtain the temperature change rate TX, temperature amplitude rate TB and temperature deviation rate TC within a specified period;

[0049] Step B2: Calculate the control analysis value

[0050] pass , calculate and obtain the control analysis value Y of the trolley furnace;

[0051] Wherein, β1, β2, and β3 are respectively the preset weight coefficients corresponding to the temperature change rate TX, the temperature amplitude rate TB, and the temperature deviation rate TC;

[0052] Step B3: Compare and determine abnormal signals

[0053] The control analysis value Y is compared with the control analysis threshold value YU to determine whether it generates an abnormal signal.

[0054] As a further solution of the present invention: the comparison method in Step B3 is as follows:

[0055] If Y ≥ YU, it means that the temperature of the trolley furnace is abnormal during the specified period, and an abnormal signal is generated;

[0056] If Y<YU, it means that the temperature of the trolley furnace is normal during the specified period and no abnormal signal is generated.

[0057] Beneficial effects of the present invention:

[0058] Precise temperature control: By real-time detection of the temperature data in the trolley furnace and establishing a relationship model between the temperature data and the power data, the power of the trolley furnace can be accurately adjusted, thereby accurately controlling the temperature in the furnace and meeting the strict temperature requirements of the production process.

[0059] Improve production efficiency: Accurate temperature control helps optimize the heat treatment process, reducing production delays and product quality issues caused by temperature deviations, thereby improving production efficiency.

[0060] Energy saving and consumption reduction: Adjusting power according to temperature deviation and relationship model avoids unnecessary high-power operation, which can effectively reduce energy consumption and save production costs.

[0061] Timely detection of abnormalities: By analyzing and processing temperature characteristics, the temperature abnormalities of the trolley furnace can be detected in time, abnormal signals can be generated and displayed to relevant personnel so that timely measures can be taken to solve the problem and avoid production accidents and product quality losses.

[0062] Optimization control strategy: The establishment of relationship model and control analysis algorithm provide a scientific basis for the control strategy of the trolley furnace, which helps to continuously optimize the control parameters and improve the performance and adaptability of the control system.

[0063] Data visualization and convenient management: The data display unit can display abnormal signals to relevant personnel, allowing managers to promptly understand the operating status of the trolley furnace and achieve convenient monitoring and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The present invention will be further described below in conjunction with the accompanying drawings.

[0065] Figure 1 The present invention is a system block diagram of a control system for an electric heating heat treatment trolley furnace.

[0066] Figure 2 It is a schematic diagram of the process of pretreatment in a control system of an electric heating heat treatment trolley furnace of the present invention.

[0067] Figure 3 It is a schematic diagram of the flow of analysis and processing in a control system of an electric heating heat treatment trolley furnace of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Embodiment 1

[0070] See also Figure 1 and Figure 3 As shown, the present invention is a control system for an electric heating heat treatment trolley furnace, comprising:

[0071] The temperature detection module is used to detect the temperature data in the trolley furnace in real time and transmit the temperature data to the data processing module;

[0072] A data processing module is used to receive the temperature data from the temperature detection module and analyze and process it;

[0073] The specific method is as follows:

[0074] StepA1, obtain the real-time temperature data within the specified period;

[0075] Step A2: Divide the specified period into several time nodes, extract the temperature data at each time node, and mark it as T i , i=1, 2, ... n, n represents the number of time nodes in the specified period;

[0076] Step A3, using the mean filtering algorithm, filtering the temperature data at each time node within the specified period, and obtaining the filtered temperature value;

[0077] The specific method is as follows:

[0078] pass , calculate and obtain the filtered temperature value TL;

[0079] StepA4, through , calculate the temperature change rate TX within the specified period;

[0080] In the formula, the calculation steps of the temperature change rate within the specified period are: first, the difference between the temperature data at each adjacent time node within the specified period is calculated, and each difference is divided by the filtered temperature value TL, and then all the results are added together, and then divided by n-1 to obtain the temperature change rate;

[0081] Step A5: Extract the temperature data with the largest and smallest values ​​from the temperature data at each time node within the specified period and mark them as T max and T min ;

[0082] StepA6. Pass , calculate the temperature amplitude rate TB within the specified period;

[0083] In the formula, the calculation steps of the temperature amplitude rate within a specified period are as follows: first, the temperature data T with the largest value is max Subtract the temperature data with the smallest value T min , and then divide the result by the filtered temperature value TL, and obtain the temperature amplitude rate TB within the specified period;

[0084] StepA7, obtain the target temperature TM set for the trolley furnace;

[0085] StepA8, through , calculate the temperature deviation rate TC;

[0086] In the formula, the calculation steps of the temperature deviation rate within the specified period are: first, the absolute value of the difference between the filtered temperature value TL and the target temperature TM is calculated, and then the result value is divided by the target temperature TM to obtain the temperature deviation rate TC within the specified period;

[0087] A data analysis unit, used to perform control analysis of the trolley furnace based on the results obtained by the data processing module;

[0088] The control analysis method is as follows:

[0089] Step B1, obtain the temperature change rate TX, temperature amplitude rate TB and temperature deviation rate TC within the specified period;

[0090] Step B2, pass , calculate and obtain the control analysis value Y of the trolley furnace;

[0091] Wherein, β1, β2, and β3 are respectively the preset weight coefficients corresponding to the temperature change rate TX, the temperature amplitude rate TB, and the temperature deviation rate TC;

[0092] Step B3, compare the control analysis value Y with the control analysis threshold value YU, and determine whether it generates an abnormal signal:

[0093] If Y ≥ YU, it means that the temperature of the trolley furnace is abnormal during the specified period, and an abnormal signal is generated;

[0094] If Y<YU, it means that the temperature of the trolley furnace is normal during the specified period and no abnormal signal is generated;

[0095] A data display unit, used to display abnormal signals to relevant personnel;

[0096] This embodiment can detect the temperature data in the trolley furnace in real time, improve the accuracy of temperature measurement through high-precision thermocouple sensors and specific measurement formulas, and perform detailed analysis and processing on the temperature data, including mean filtering, calculation of temperature change rate, temperature amplitude rate and temperature deviation rate, so as to fully understand the temperature change situation, perform control analysis based on the analysis results, and promptly discover abnormal temperature conditions of the trolley furnace, generate abnormal signals and display them to relevant personnel through a data display unit, which helps to take timely measures to ensure production quality and safety.

[0097] Embodiment 2

[0098] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes: a data acquisition unit and a data control unit;

[0099] The data acquisition unit is used to obtain the operating data of the trolley furnace, and the operating data includes temperature data under different power data;

[0100] The data processing unit is used to pre-process the operation data;

[0101] The method is as follows:

[0102] Step M1, obtain temperature data and power data in real time within a specific period of time;

[0103] Step M2, divide the specified period into several collection nodes, extract the temperature data and power data on each collection node, and mark them as Te and Pe respectively, e=1, 2, ... k, k represents the number of collection nodes in a specific period;

[0104] Step M3: Build a relationship model between temperature data and power data through linear regression algorithm ;

[0105] Among them, a and b are the relationship coefficients to be confirmed;

[0106] The relationship coefficients a and b are determined as follows:

[0107] pass: and , determine the specific values ​​of the relationship coefficients a and b;

[0108] Where Ph and Th are the average values ​​of all Pe and Te respectively;

[0109] The data analysis unit is used to perform control analysis based on the relationship model obtained by the data processing unit;

[0110] The control analysis method is as follows:

[0111] Step K1, obtain the current temperature, current power, and target temperature of the trolley furnace, and mark them as T1, P1, and T0 respectively;

[0112] Step K2, calculate the current temperature deviation value T2 of the trolley furnace through T2=T0-T1;

[0113] Step K3, according to the relationship model obtained by the data processing unit, reverse it, and then combine the current temperature deviation value of the trolley furnace to calculate the power adjustment amount P2 required to reach the target temperature;

[0114] The power adjustment required to achieve the target temperature is calculated as: ;

[0115] Step K4, calculate the current power setting value Ps of the trolley furnace through Ps=P1+P2;

[0116] The data control unit is used to reset the power of the trolley furnace according to the power setting value obtained by the data analysis unit;

[0117] This embodiment adds a data acquisition unit to obtain temperature data under different power data, enriches the data dimension, and makes the analysis of the trolley furnace more comprehensive. By preprocessing and constructing a relationship model between temperature data and power data, it can more accurately predict the power adjustment required to reach the target temperature. The data control unit resets the power of the trolley furnace according to the calculated power adjustment value, thereby achieving precise control of the trolley furnace power, which helps to improve production efficiency and product quality.

[0118] Embodiment 3

[0119] As the third embodiment of the present invention, when the present application is implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments;

[0120] This embodiment combines the advantages of embodiment one and embodiment two, realizes comprehensive monitoring and analysis of the trolley furnace temperature, and precise control of power, can more effectively ensure the stable operation of the trolley furnace, improve the controllability of the production process and the stability of product quality, provide operators with more comprehensive and accurate information, reduce the risk of failure, and improve production efficiency and economic benefits.

[0121] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0122] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An electric heating heat treatment trolley furnace control system, characterized in that: include: The temperature detection module is used to detect the temperature data in the trolley furnace in real time and transmit the temperature data to the data processing module; A data acquisition unit is used to obtain the operating data of the trolley furnace, and the operating data includes temperature data under different power data; A data processing unit, used for preprocessing the operating data and obtaining a relationship model between the temperature data and the power data according to the preprocessing result; The preprocessing method is as follows: Step M1, obtain temperature data and power data in real time within a specific period of time; Step M2, divide the specified period into several collection nodes, extract the temperature data and power data on each collection node, and mark them as Te and Pe respectively, e=1, 2, ... k, k represents the number of collection nodes in a specific period; Step M3: Build a relationship model between temperature data and power data through linear regression algorithm ; Among them, a and b are the relationship coefficients to be confirmed; The relationship coefficients a and b are determined as follows: pass: and , determine the specific values ​​of the relationship coefficients a and b; Where Ph and Th are the average values ​​of all Pe and Te respectively; The data analysis unit is used to perform control analysis based on the relationship model obtained by the data processing unit, and then determine the current power setting value of the trolley furnace; The control analysis method is as follows: Step K1, obtain the current temperature, current power and target temperature of the trolley furnace; Step K2, calculate the current temperature deviation value of the trolley furnace by subtracting the current temperature from the target temperature; Step K3, according to the relationship model obtained by the data processing unit, reverse it, and then combine it with the current temperature deviation value of the trolley furnace to calculate the power adjustment required to reach the target temperature; Step K4, by adding the current power and the power adjustment amount, calculate the current power setting value of the platform furnace; The data control unit is used to reset the power of the trolley furnace according to the power setting value obtained by the data analysis unit.

2. The electric heating heat treatment trolley furnace control system according to claim 1 is characterized in that: The power adjustment required to achieve the target temperature is calculated as: ； Where T2 refers to the current temperature deviation of the trolley furnace, and P2 refers to the power adjustment required to reach the target temperature.

3. The electric heating heat treatment trolley furnace control system according to claim 1 is characterized in that: It also includes a data presentation unit; The data processing module is used to receive the temperature data from the temperature detection module, analyze and process the data, and extract the corresponding temperature characteristics; The temperature characteristics include the temperature change rate, temperature amplitude rate and temperature deviation rate corresponding to the temperature data in the trolley furnace; The data analysis unit is used to perform control analysis of the trolley furnace according to the results obtained by the data processing module, determine whether the trolley furnace is abnormal, and generate an abnormal signal according to the abnormal state of the trolley furnace; The data display unit is used to display the abnormal signal to relevant personnel.

4. The electric heating heat treatment trolley furnace control system according to claim 3 is characterized in that: The analysis and processing methods are as follows: StepA1, obtain the temperature data in real time within the specified period; StepA2, divide the specified period into several time nodes, extract the temperature data at each time node, and mark it as Ti, i=1, 2, ... n, n represents the number of time nodes in the specified period; Step A3, filtering the temperature data at each time node within the specified period, and obtaining the filtered temperature value TL; Step A4, calculate the difference between the temperature data at each adjacent time node within the specified period, and divide each difference by the filtered temperature value TL, then add all the results, and then divide by n-1 to obtain the temperature change rate TX within the specified period; Step A5: Extract the temperature data with the largest and smallest values ​​from the temperature data at each time node within the specified period and mark them as T max and T min ; StepA6, the temperature data with the largest value T max Subtract the temperature data with the smallest value T min , and then divide the result by the filtered temperature value TL, and obtain the temperature amplitude rate TB within the specified period; StepA7, obtain the target temperature TM set for the trolley furnace; Step A8, calculate the absolute value of the difference between the filtered temperature value TL and the target temperature TM, then divide the result value by the target temperature TM, and obtain the temperature deviation rate TC within the specified period.

5. The control system of an electric heating heat treatment trolley furnace according to claim 4, characterized in that: The filtering process adopts a mean filtering algorithm, and the average value between the temperature data at each time node is the filtered temperature value TL.

6. The electric heating heat treatment trolley furnace control system according to claim 5, characterized in that: The control analysis method is as follows: Step B1, obtain the temperature change rate TX, temperature amplitude rate TB and temperature deviation rate TC within the specified period; Step B2, pass , calculate and obtain the control analysis value Y of the trolley furnace; Wherein, β1, β2, and β3 are respectively the preset weight coefficients corresponding to the temperature change rate TX, the temperature amplitude rate TB, and the temperature deviation rate TC; Step B3: Compare the control analysis value Y with the control analysis threshold value YU and determine whether an abnormal signal is generated.

7. The electric heating heat treatment trolley furnace control system according to claim 6 is characterized in that: The comparison method in StepB3 is as follows: If Y ≥ YU, it means that the temperature of the trolley furnace is abnormal during the specified period, and an abnormal signal is generated; If Y<YU, it means that the temperature of the trolley furnace is normal during the specified period and no abnormal signal is generated.

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