Temperature control method and system for hot roller process

Abstract

The invention discloses a temperature control method and system for a hot roller process, and the method comprises the steps: S1, dividing a roller body into a plurality of subsection control intervals in the axial direction, and employing the temperature sampling data of each subsection control interval; s2, predicting a temperature field of each subsection control interval based on a thermodynamic simulation model, and fitting the temperature field of the subsection control interval by using the temperature sampling data corresponding to each subsection control interval and the temperature field change by taking the temperature uniformity of the roller body in the axial direction as a target; and S3, adjusting the temperature of each subsection control interval by using the temperature field corresponding to each subsection control interval.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a temperature control method and system for hot roller processes. Background Technology

[0002] As a crucial technology in the rolling process of lithium battery manufacturing, the hot roll process will continue to be an important direction for lithium battery research and industrial development as the requirements for battery performance and manufacturing costs continue to increase.

[0003] In existing technologies, the hot roll process has the following drawbacks: the temperature of the rolls cannot be precisely controlled during production, and uneven temperature distribution is prone to occur. Uneven temperature distribution leads to uneven heating of the electrode materials, which in turn affects the rearrangement and compaction of active materials, conductive agents, and binder particles; uneven temperature distribution also causes uneven stress on the electrodes during rolling, potentially resulting in surface ripples, thickness deviations, or localized defects, thus affecting battery assembly and long-term stability; uneven temperature distribution may also cause electrode materials to adhere to the rolls during rolling, further impacting product quality and production efficiency. Summary of the Invention

[0004] This invention provides a temperature control method and system for hot roller processes, aiming to solve at least one defect in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a temperature control method for a hot roller process, comprising:

[0006] S1. Divide the roller body into several segmented control intervals in the axial direction, and use the temperature sampling data of each segmented control interval;

[0007] S2. Based on the thermodynamic simulation model, predict the temperature field of each segmented control interval, with the goal of uniform temperature of the roller body in the axial direction, and fit the temperature field of the segmented control interval using the temperature sampling data and temperature field changes corresponding to each segmented control interval.

[0008] S3. Adjust the temperature of each segmented control interval using the temperature field corresponding to each segmented control interval.

[0009] Optionally, the thermodynamic simulation model is:

[0010]

[0011] Where ρ(T) is the density of the roller material at time T, T is the temperature field, λ(T) is the thermal conductivity λ of the roller material at time T, cp(T) is the specific heat capacity c of the roller material at time T, q represents the interfacial heat flux density, x, y, and z are the axial, circumferential, and radial coordinates of the roller, respectively, and t is the time variable. It represents a partial differential.

[0012] Optionally, S2 also includes:

[0013] Set the first boundary conditions:

[0014] T α =T β +R×q

[0015] Set a second boundary condition:

[0016] q loss =q meas

[0017] Set a third boundary condition:

[0018] q=h(T) r -T ev )+εσ(T r 4 -T ev 4 )

[0019] In the formula, T α T represents the surface temperature of the roller body at the contact point with the material. β The material surface temperature at the contact surface between the roller and the material is represented by R, the contact thermal resistance by h, the convective heat transfer coefficient by h, and the interfacial heat flux density by q. loss q represents the heat flux density of the roller body at the contact boundary between the roller body and the bearing. meas T represents the measured heat flux density value. r T represents the real-time temperature of the non-contact surface of the roller. ev ε represents ambient temperature, ε represents emissivity, and σ represents the Stefan-Boltzmann constant.

[0020] Optionally, fitting the temperature field of each segmented control interval using the temperature sampling data and temperature field changes corresponding to that segmented control interval includes:

[0021] Construct the objective function:

[0022] J=∑ω i (T) i -T exi ) 2

[0023] The temperature field T is calculated using the thermodynamic simulation model under the first, second, and third boundary conditions. i , will T exi and T i If J > 1℃², then adjust the convective heat transfer coefficient h and the contact thermal resistance R until J ≤ 1℃².

[0024] Optionally, S3 includes:

[0025] The temperature field T is calculated using the aforementioned thermodynamic simulation model. i Based on the temperature field T i Determine the target heat flux density q for the i-th segmented control interval. tai ;

[0026] Through the target heat flux density q tai The heating power Pi of the i-th segmented control interval is determined using the following formula:

[0027] P i =q tai ×A / η

[0028] q tai =k×(Tta-T i )+q loss

[0029] In the formula, A represents the heating area of ​​the segmented control zone, and η represents the thermal efficiency of the heating device.

[0030] Optionally, based on the roller structure, at least three segmented control intervals are divided along the axial direction of the roller at equal intervals.

[0031] Secondly, embodiments of the present invention also provide a temperature control system for a hot roller process, including a controller configured to execute any of the temperature control methods for a hot roller process described in the embodiments of the present invention.

[0032] Optionally, it may also include several embedded sensors, which are connected to the controller;

[0033] An embedded sensor is arranged at equal intervals along the axial direction of the roller.

[0034] Optionally, a resistance wire is also included, and the controller is connected to the resistance wire and configured to heat the roller body through the resistance wire.

[0035] Optionally, the system may also include a server, which is communicatively connected to the controller and configured to archive control process data of the controller.

[0036] Compared with existing technologies, the advantages of this invention are as follows: This invention proposes a temperature control method for hot roller processes. In this method, the roller body is divided into several segmented control intervals along its axial direction. Precise control can be achieved based on independent temperature sampling data for each interval. Temperature compensation for each interval is realized by combining the temperature field fitted by a thermodynamic simulation model, thereby controlling the axial temperature difference of the roller body within a preset range and improving the temperature uniformity of the roller body. In this method, the temperature field change trend of each segmented control interval is predicted in advance by using a thermodynamic simulation model. Simultaneously, by combining the temperature sampling data with the temperature field fitted to each segmented control interval, the accuracy of the calculated temperature field value can be improved, thus ensuring the accuracy of roller body temperature control. Attached Figure Description

[0037] Figure 1 This is a flowchart of the temperature control method for the hot roller process in the embodiment;

[0038] Figure 2 This is a schematic diagram of the installation of the embedded sensor in the embodiment;

[0039] Figure 3 This is a block diagram of the temperature control system in the embodiment. Detailed Implementation

[0040] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0041] Example 1

[0042] Figure 1 This is a flowchart of the temperature control method for the hot roller process in the embodiment, for reference. Figure 1 The methods include:

[0043] S1. Divide the roller body into several segmented control intervals along the axial direction, and use the temperature sampling data of each segmented control interval.

[0044] S2. Based on the thermodynamic simulation model, predict the temperature field of each segmented control interval. With the goal of uniform temperature of the roller in the axial direction, fit the temperature field of the segmented control interval using the temperature sampling data and temperature field changes corresponding to each segmented control interval.

[0045] S3. Adjust the temperature of each segmented control interval using the temperature field corresponding to each segmented control interval.

[0046] For example, in this solution, the roller body is divided into multiple independent segmented control intervals along the axial direction (length direction) (such as uniformly divided according to the length of the roller body, or non-uniformly divided according to the actual temperature sensitive area). Each interval is equipped with an independent temperature detection unit (such as a temperature sensor) to collect temperature sampling data of each interval in real time, providing a data basis for subsequent temperature control.

[0047] For example, in this solution, one or more thermocouples can be set up in each segmented control interval. When multiple thermocouples are configured, the average value of the sampled data is taken as the temperature sampling data for that segmented control interval. Each segmented control interval is configured with an independent heating device to regulate the temperature of the corresponding segmented control interval.

[0048] In this scheme, the (theoretical) temperature field of each segmented control interval is predicted by a thermodynamic simulation model. The thermodynamic simulation model can be constructed by finite element analysis based on parameters such as roller material, size, thermal conductivity, ambient temperature, and initial temperature.

[0049] In this scheme, with the goal of uniform axial temperature of the roller, the real-time temperature sampling data of each segment control interval is compared with the theoretical temperature field predicted by the simulation model. The theoretical temperature field is corrected by fitting algorithms such as least squares method and neural network to obtain the actual temperature field of each segment control interval.

[0050] In this scheme, for the actual temperature field of each segmented control interval, the temperature control execution unit (such as heating tube) of that segmented control interval is independently adjusted so that the temperature field of each segmented control interval converges towards the axial uniform target.

[0051] This embodiment proposes a temperature control method for hot roller processes. In this method, the roller body is divided into several segmented control intervals along its axial direction. Precise control can be achieved based on independent temperature sampling data for each interval. Temperature compensation for each interval is realized by combining the temperature field fitted by a thermodynamic simulation model, thereby controlling the axial temperature difference of the roller body within a preset range and improving the temperature uniformity of the roller body. In this method, the temperature field change trend of each segmented control interval is predicted in advance by using a thermodynamic simulation model. Simultaneously, by combining the temperature sampling data with the temperature field fitted to each segmented control interval, the accuracy of the calculated temperature field value can be improved, thus ensuring the accuracy of the roller body temperature control.

[0052] Based on any of the aforementioned schemes, in one feasible implementation, the thermodynamic simulation model is as follows:

[0053]

[0054] Where ρ(T) is the density of the roller material at time T, T is the temperature field, λ(T) is the thermal conductivity of the roller material at time T, cp(T) is the specific heat capacity of the roller material at time T, q represents the interfacial heat flux density, x, y, and z are the axial, circumferential, and radial coordinates of the roller, respectively, and t is the time variable. It represents a partial differential.

[0055] In this scheme, a thermodynamic simulation model is established by combining the roller structure parameters, material thermophysical parameters (ρ(T), cp(T), λ(T)) and interface heat flux density q. The model can be solved by the finite element method to predict the theoretical temperature field of each segment control interval.

[0056] In this scheme, ρ(T) and cp(T) reflect the temperature change trend, x, y, and z describe the heat transfer process of the roller in the axial, circumferential, and radial directions, λ(T) reflects the change of thermal conductivity with temperature, and q represents the external factors affecting the temperature field change, such as the heating / cooling heat flow of the temperature control execution unit and the heat exchange with the external environment. The thermodynamic simulation model can accurately describe the dynamic change law of the roller temperature field and improve the prediction accuracy of the temperature field.

[0057] Based on any of the aforementioned schemes, in one possible implementation, S2 further includes:

[0058] Set the first boundary conditions:

[0059] T α =T β +R×q

[0060] Set a second boundary condition:

[0061] q loss =q meas

[0062] Set a third boundary condition:

[0063] q=h(T) r -T ev )+εσ(T r 4 -T ev 4 )

[0064] In the formula, T α T represents the surface temperature of the roller body at the contact point with the material. β The material surface temperature at the contact surface between the roller and the material is represented by R, the contact thermal resistance by h, the convective heat transfer coefficient by h, and the interfacial heat flux density by q. loss q represents the heat flux density of the roller body at the contact boundary between the roller body and the bearing. meas T represents the measured heat flux density value. rT represents the real-time temperature of the non-contact surface of the roller. ev ε represents ambient temperature, ε represents emissivity, and σ represents the Stefan-Boltzmann constant.

[0065] In this scheme, the first boundary condition quantifies the influence of contact thermal resistance on the surface temperature of the roller. The first boundary condition is used to ensure that the internal temperature of the roller is consistent with the temperature of the material contact surface, so as to ensure that the temperature control meets the actual needs of the process.

[0066] In this scheme, the second boundary condition represents the heat loss (roller heat flux density) at the contact boundary between the roller and the bearing by the actual measured heat flux density value, thereby reducing the error in temperature field prediction caused by incorrect estimation of heat loss.

[0067] In this scheme, the third boundary condition quantifies the influence of ambient temperature fluctuations and roller surface temperature on interfacial heat flux density, and uses ambient heat transfer as a limiting factor for temperature field prediction, which can improve the accuracy of modeling.

[0068] For example, in this scheme, the initial values ​​of the contact thermal resistance R, the initial value of the convective heat transfer coefficient h, and the emissivity ε can be determined through calibration tests, and the material surface temperature T... β The heat flux density value q can be obtained by measuring with a thermometer. meas The ambient temperature T can be obtained by measuring a heat flux sensor. ev This can be obtained through temperature sensor measurement.

[0069] In this scheme, boundary conditions are set from three aspects: contact thermal resistance, heat loss and environmental heat transfer. The contact thermal resistance, heat loss and environmental heat transfer are quantified, which can reduce the error in temperature field prediction caused by empirical estimation, thereby improving the accuracy of roller surface temperature control.

[0070] Based on any of the aforementioned schemes, in one possible implementation, fitting the temperature field of each segmented control interval using the temperature sampling data and temperature field changes corresponding to that segmented control interval includes constructing an objective function:

[0071] J=∑ω i (T) i -T exi ) 2

[0072] The temperature field T of the i-th piecewise control interval was calculated using a thermodynamic simulation model under the first, second, and third boundary conditions. i , will T exi and T i Substituting these values ​​into the objective function, if J > 1℃², then adjust the convective heat transfer coefficient h and the contact thermal resistance R until J ≤ 1℃², T exi This represents the temperature sampling value corresponding to the i-th segmented control interval.

[0073] In this scheme, an objective function is constructed based on the deviation between the predicted temperature field and the measured temperature field. The temperature deviation of multiple segmented control intervals is transformed into a single judgment value J through a weighted sum of squares. The accuracy of temperature field prediction is ensured by the constraint condition of the J value.

[0074] In this scheme, during the iteration process, the gradient descent method can be used to adjust h and R in the direction of decreasing J value so that J can meet the constraint conditions. By adjusting the two parameters h and R (which have a significant impact on temperature field prediction), the problem of low computational efficiency caused by blind iteration of multiple parameters can be avoided.

[0075] In this scheme, by setting an objective function, a quantifiable accuracy standard can be provided for the fitting of the temperature field, so that the accuracy of the temperature field prediction is kept within an acceptable range to meet the established process requirements.

[0076] Based on any of the aforementioned schemes, in one possible implementation, S3 includes:

[0077] The temperature field Ti was calculated using a thermodynamic simulation model, and based on the temperature field T... i Determine the target heat flux density q for the i-th segmented control interval. tai ; through the target heat flux density q tai The heating power Pi of the i-th segmented control interval is determined by the following formula:

[0078] P i =q tai ×A / η

[0079] q tai =k×(T ta -T i )+q loss

[0080] In the formula, A represents the heating area of ​​the segmented control zone, η represents the thermal efficiency of the heating device, and T ta This represents the target temperature field.

[0081] In this scheme, the fitted actual temperature field T is used. i With the preset target temperature field T ta The core is the deviation, combined with the heat loss q loss The target heat flux density q for each interval is calculated using the proportionality coefficient k. tai This can compensate for the current temperature deviation and offset the continuous heat loss in each zone.

[0082] The target heat flux density q tai The heating area A and thermal efficiency η are combined to convert the actual power P of the heating device. iThis ensures that the actual effect of heating control is consistent with the target value, avoiding control deviations caused by heat efficiency loss.

[0083] In this scheme, the temperature field deviation is quantified into a controllable power parameter, which facilitates temperature control. Temperature control can be achieved by adjusting the heating power, thereby improving control accuracy and meeting the predetermined process requirements.

[0084] Based on any of the aforementioned schemes, in one possible implementation, based on the roller structure, at least three segmented control intervals are divided along the axial direction of the roller by a network at equal intervals.

[0085] In this scheme, the axial length of the roller body is used as the reference, and the grid is divided at a fixed interval to ensure that the axial length of each segment control zone is consistent. At the same time, the number of segments is ≥3, covering both ends and the middle area of ​​the roller body, so as to avoid local temperature runaway due to too few segments.

[0086] In this scheme, the equal-interval division can ensure that the heating area, heat conduction path and boundary conditions of each interval are consistent, which facilitates the reuse of parameters in the thermodynamic simulation model and the standardization of the control algorithm, while simplifying the hardware layout.

[0087] Based on any of the aforementioned solutions, in one possible implementation, the method includes:

[0088] S1. Divide the roller body into several segmented control intervals along the axial direction, and use the temperature sampling data of each segmented control interval.

[0089] S2. Based on the thermodynamic simulation model, predict the temperature field of each segmented control interval. With the goal of uniform temperature of the roller in the axial direction, fit the temperature field of the segmented control interval using the temperature sampling data and temperature field changes corresponding to each segmented control interval.

[0090] S3. Adjust the temperature of each segmented control interval using the temperature field corresponding to each segmented control interval.

[0091] In this scheme, the thermodynamic simulation model is as follows:

[0092] .

[0093] The boundary conditions of the thermodynamic simulation model include: T α =T β +R×q;q loss =q meas ;q=h(T) r -T ev )+εσ(T r 4 -T ev 4 ).

[0094] The objective function of the thermodynamic simulation model includes: J = ∑ωi (T i -T exi ) 2 .

[0095] In this scheme, the temperature field T is calculated using a thermodynamic simulation model. i Based on temperature field T i Determine the target heat flux density q for the i-th segmented control interval. tai ; through the target heat flux density q tai Determine the heating power P of the i-th segmented control interval. i The formula used is:

[0096] P i =q tai ×A / η

[0097] q tai =k×(T ta -T i )+q loss .

[0098] For example, in this solution, based on the structure and thermal characteristics of the roller body, N segmented control intervals are divided along the axial direction, such that the temperature gradient within each segmented control interval is ≤0.5℃ / cm. For instance, for a certain type of roller body, 3 segmented control intervals are divided along the axial direction.

[0099] In this solution, an embedded (temperature) sensor is installed inside the roller at the corresponding position in each segment. The embedded sensor can be placed near the surface of the roller to ensure that it can accurately collect the temperature of the working layer of the roller.

[0100] In this scheme, it is assumed that ρ(T) i )=7850-0.2×(T i -25); λ(T)=32×[1-0.0015×(T i -25)];cp(T)=460×[1+0.0008×ln(T i / 25)).

[0101] In this scheme, q and T are defined. α T β q loss q meas ε, σ, ω i The initial values ​​of R and h are given.

[0102] In this scheme, the finite difference method is used to discretize and solve the thermodynamic simulation model to obtain the initial predicted temperature field T for each interval. iCalculate J based on the objective function. If J > 1℃², then use the gradient descent method to adjust the convective heat transfer coefficient h and the contact thermal resistance R until J ≤ 1℃². The temperature field T at this point is... i To achieve a precise temperature field after fitting.

[0103] Fit the temperature field T i Then, through P i =q tai Calculate the corresponding heating power P using ×A / η. i , will P i As the target value, a closed-loop (e.g., PID) control method is used to control the heating power of the heating device in each segment control interval. When the heating power reaches P... i At that time, adjust q tai =q loss The S1~S3 cycle is executed repeatedly to achieve stable temperature control.

[0104] Example 2

[0105] This embodiment proposes a temperature control system for hot roller process, including a controller. The controller is configured to execute any of the temperature control methods for hot roller process described in Embodiment 1. The implementation process and beneficial effects of the method are the same as the corresponding content described in Embodiment 1, and the beneficial effects are also the same. The specific details will not be described in detail.

[0106] Based on any of the aforementioned schemes, in one possible implementation, a number of embedded sensors are further included, which are connected to the controller; an embedded sensor is arranged in a network at equal intervals along the axial direction of the roller.

[0107] Figure 2 This is a schematic diagram of the installation of the embedded sensor in the embodiment, for reference. Figure 2 In this scheme, based on the roller structure, the roller is divided into three segmented control intervals at equal intervals along its axial direction, and an embedded sensor is set in each segmented control interval.

[0108] In this scheme, the embedded sensors are arranged at equal intervals to correspond to the segmented control intervals, which can ensure that the temperature sampling points in each segmented control interval are spatially uniformly distributed, and avoid temperature field fitting errors caused by insufficient sampling density in local areas.

[0109] For example, in this solution, in order to cover the temperature gradient of the segmented control intervals, multiple embedded sensors can be set in each segmented control interval, and the embedded sensors in each segmented control interval are arranged at equal intervals.

[0110] For example, in this solution, the embedded sensor and the controller are connected via wired or wireless means. The embedded sensor sends the temperature signal collected in real time to the controller. The controller performs preprocessing such as filtering and averaging on the temperature signal, and then fits the temperature field in combination with a thermodynamic simulation model.

[0111] Based on any of the aforementioned solutions, in one possible implementation, a resistance wire is further included, and a controller is connected to the resistance wire, the controller being configured to heat the roller body through the resistance wire.

[0112] In this solution, resistance wire heating has the advantages of simple structure, stable temperature control and low cost. It can form a precise linkage with the sampling data of equally spaced embedded sensors, and can be well adapted to the application scenarios of hot roller process at medium and low temperature (≤300℃).

[0113] Based on any of the aforementioned schemes, in one possible implementation, a server is further included. The server is communicatively connected to the controller and is configured to archive the controller's control process data.

[0114] In this solution, a server is configured for the system, and a communication connection is established between the server and the controller. The server is used to implement a mechanism for archiving and managing control data in temperature control, thereby achieving traceability of the temperature control process.

[0115] Figure 3 This is a block diagram of the temperature control system in the embodiment, for reference. Figure 2 and Figure 3 Based on any of the aforementioned solutions, in one possible implementation, the system includes:

[0116] The controller 100, embedded sensor 200, and resistance wire 300 are arranged in the corresponding segmented control range on the roller body. The embedded sensor 200 and resistance wire 300 are electrically connected to the controller 100.

[0117] In this scheme, the controller 100 is configured to fit the temperature field through a thermodynamic simulation model and temperature sampling data from the embedded sensor 200, and then calculate the target heating power required for each segment interval. The controller 100 outputs a power adjustment command based on the target heating power, and then controls the temperature of each segment control interval by adjusting the supply voltage or current of the resistance wire 300.

[0118] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A temperature control method for a hot roller process, characterized in that, include: S1. Divide the roller body into several segmented control intervals in the axial direction, and use the temperature sampling data of each segmented control interval; S2. Based on the thermodynamic simulation model, predict the temperature field of each segmented control interval, with the goal of uniform temperature of the roller body in the axial direction, and fit the temperature field of the segmented control interval using the temperature sampling data and temperature field changes corresponding to each segmented control interval. S3. Adjust the temperature of each segmented control interval using the temperature field corresponding to each segmented control interval.

2. The temperature control method for hot roller process as described in claim 1, characterized in that, The thermodynamic simulation model is as follows: Where ρ(T) is the density of the roller material at time T, T is the temperature field, λ(T) is the thermal conductivity of the roller material at time T, cp(T) is the specific heat capacity of the roller material at time T, q represents the interfacial heat flux density, x, y, and z are the axial, circumferential, and radial coordinates of the roller, respectively, and t is the time variable. It represents a partial differential.

3. The temperature control method for hot roller process as described in claim 2, characterized in that, S2 also includes: Set the first boundary conditions: T α =T β +R×q Set a second boundary condition: q loss =q meas Set a third boundary condition: q=h(T r -T ev )+εσ(T r 4 -T ev 4 ) In the formula, T α T represents the surface temperature of the roller body at the contact point with the material. β The material surface temperature at the contact surface between the roller and the material is represented by R, the contact thermal resistance by h, the convective heat transfer coefficient by h, and the interfacial heat flux density by q. loss q represents the heat flux density of the roller body at the contact boundary between the roller body and the bearing. meas T represents the measured heat flux density value. r T represents the real-time temperature of the non-contact surface of the roller. ev ε represents ambient temperature, ε represents emissivity, and σ represents the Stefan-Boltzmann constant.

4. The temperature control method for hot roller process as described in claim 3, characterized in that, Fitting the temperature field of each segmented control interval using temperature sampling data and temperature field changes includes: Construct the objective function: J=∑ω i (T i -T exi ) 2 Under the first, second, and third boundary conditions, the temperature field T of the i-th segmented control interval is calculated using the thermodynamic simulation model. i , will T exi and T i Substituting these values ​​into the objective function, if J > 1℃², then adjust the convective heat transfer coefficient h and the contact thermal resistance R until J ≤ 1℃², T exi This represents the temperature sampling value corresponding to the i-th segmented control interval.

5. The temperature control method for hot roller process as described in claim 4, characterized in that, S3 include: The temperature field T is calculated using the aforementioned thermodynamic simulation model. i Based on the temperature field T i Determine the target heat flux density q for the i-th segmented control interval. tai ; Through the target heat flux density q tai The heating power Pi of the i-th segmented control interval is determined using the following formula: P i =q tai ×A / η q tai =k×(T ta -T i )+q loss In the formula, A represents the heating area of ​​the segmented control zone, η represents the thermal efficiency of the heating device, and T ta This represents the target temperature field.

6. The temperature control method for hot roller process according to any one of claims 1 to 5, characterized in that, Based on the roller structure, at least three segmented control intervals are divided along the axial direction of the roller at equal intervals.

7. A temperature control system for hot roller processes, characterized in that, Includes a controller configured to perform the method according to any one of claims 1 to 6.

8. The temperature control system for hot roller process as described in claim 7, characterized in that, It also includes several embedded sensors, which are connected to the controller; An embedded sensor is arranged at equal intervals along the axial direction of the roller.

9. The temperature control system for hot roller process as described in claim 7, characterized in that, It also includes a resistance wire, and the controller is connected to the resistance wire and configured to heat the roller body through the resistance wire.

10. The temperature control system for hot roller process as described in claim 7, characterized in that, It also includes a server, which is communicatively connected to the controller and configured to archive the controller's control process data.

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