A method and system for intelligent temperature control of dough proofing equipment

Abstract

This invention relates to the field of temperature control, and more particularly to an intelligent temperature control method and system for dough proofing equipment. The method includes the following steps: during a spraying operation inside the proofing chamber, acquiring a temperature gradient characterizing the non-uniformity of temperature distribution within the chamber; based on the temperature gradient, correcting an original temperature deviation calculated from the overall temperature inside the chamber and the theoretical temperature to obtain a corrected temperature deviation; calculating the total heat removed by the spraying operation; and based on the total heat removed by the spray and the corrected temperature deviation, performing a control compensation to offset the influence of the spray on the temperature, reduce the impact of the spray on the temperature, and improve the temperature control accuracy of the dough proofing process.

Description

Technical Field

[0001] This invention relates to the field of temperature control, and in particular to a method and system for intelligent temperature control of dough proofing equipment. Background Technology

[0002] In the industrial production of whole grain flour products, proofing is a crucial process. Proofing boxes provide a constant, high-temperature, and high-humidity environment to promote yeast fermentation, thereby giving the product its ideal volume, flavor, and texture. Temperature and humidity during the proofing process are two closely coupled and mutually influential key control variables. Currently, the industry commonly uses PID (Proportional-Integral-Derivative) control algorithms to perform closed-loop control of temperature and humidity separately.

[0003] However, in actual production, this method of decoupling and independently controlling temperature and humidity has a fundamental technical flaw. During the proofing process, when the humidity falls below the set value, the control system triggers the spray device to increase the humidity inside the chamber. But the spraying operation itself is a strong thermodynamic disturbance: the low-temperature atomized water droplets and the external air they carry rapidly absorb heat from inside the proofing chamber, causing the temperature inside the chamber to drop instantly.

[0004] Existing PID temperature control systems are designed to handle relatively slow temperature changes caused by natural heat loss or fluctuations in heating element performance. However, when faced with drastic, non-linear, and predictable external disturbances such as those encountered in spraying operations, their response is often lagging. The system only begins passive compensation adjustments after a significant temperature drop has already occurred. This delayed compensation not only causes the aging temperature to deviate from the optimal process within a short period, affecting product quality, but may also lead to oscillations in the control system due to overcompensation, further disrupting the stability of the temperature field.

[0005] Therefore, accurately quantifying the disturbance caused by spraying operations to the temperature of the proofing chamber, and designing a feedforward control strategy that can predict and actively compensate for the disturbance in advance to eliminate or reduce the coupling effect of humidity regulation on temperature control, is a key technical problem that urgently needs to be solved to improve the automation level of the proofing process and the stability of product quality. Summary of the Invention

[0006] To address the issue of compensating for the temperature effects caused by spraying, this invention provides a method and system for intelligent temperature control in dough proofing equipment.

[0007] In a first aspect, the present invention provides a method for intelligent temperature control of a dough proofing device, which adopts the following technical solution:

[0008] A method for intelligent temperature control of a dough proofing device, comprising the following steps:

[0009] a. When performing a spraying operation inside the proofing chamber, obtain a temperature gradient that characterizes the non-uniformity of temperature distribution inside the chamber;

[0010] b. Based on the temperature gradient, the original temperature deviation calculated from the combined temperature inside the chamber and the theoretical temperature is corrected to obtain a corrected temperature deviation;

[0011] c. Calculate the total heat removed by the spraying operation;

[0012] d. Based on the total heat removed by the spray and the corrected temperature deviation, perform a control compensation to counteract the effect of the spray on the temperature.

[0013] This invention upgrades the control mode from passive response to active prediction, which can offset the temperature drop caused by spraying in advance and accurately, significantly improving the stability and control accuracy of the temperature in the proofing box.

[0014] Preferably, the method for correcting the original temperature deviation is to multiply the original temperature deviation by a correction factor that is proportional to the temperature gradient.

[0015] This invention corrects the original temperature deviation by introducing a temperature gradient, thus solving the problem of inaccurate temperature deviation calculation caused by uneven temperature distribution inside the chamber due to spraying.

[0016] Preferably, the method for calculating the total heat removed by the spraying operation includes:

[0017] Calculate the sensible heat loss caused by direct heat absorption by the spray, the latent heat loss caused by spray vaporization, and the convective loss caused by air introduced by the spray, and sum the three together.

[0018] This invention constructs a complete and accurate thermodynamic perturbation model by decomposing the heat loss caused by spraying into three physical dimensions: sensible heat, latent heat, and convection.

[0019] Preferably, the sensible heat loss is calculated based on the spray mass, the specific heat capacity of water, and the temperature difference between the overall temperature inside the chamber and the spray temperature; the latent heat loss is calculated based on the spray mass and the latent heat of vaporization of water; and the convection loss is calculated based on the volume of external air brought in, the air density, the specific heat capacity of the air, and the temperature difference between the external air temperature and the overall temperature inside the chamber.

[0020] Preferably, the method for performing a single control compensation includes:

[0021] Normalize the total heat carried away by the spray to obtain a spray effect level;

[0022] The degree of spray impact is compared with a preset impact threshold to select whether to perform weak interference compensation or strong interference compensation.

[0023] Preferably, the weak interference compensation is achieved through an independent heating system that provides compensation heat that is related to the total heat carried away by the spray and decays over time.

[0024] This invention uses an independent, open-loop heating system that takes into account heat decay for compensation. It can accurately fill the heat gap caused by small spray volume without changing the stability of the main controller, thus achieving seamless compensation for minor disturbances.

[0025] Preferably, the strong interference compensation is achieved by adjusting the parameters of a PID algorithm used for temperature control, the adjustment including:

[0026] Based on the degree of spray impact and the corrected temperature deviation, the proportional coefficient of the PID algorithm is increased;

[0027] Based on the degree of spray impact, the integral coefficient of the PID algorithm is reduced;

[0028] Based on the degree of spray influence and the rate of change of the corrected temperature deviation, the differential coefficient of the PID algorithm is increased.

[0029] This invention adjusts the three key parameters P, I, and D in real time and non-linearly based on the degree of spray impact and temperature deviation, enabling the PID controller to predict the intensity and nature of spray disturbances and thus make the optimal control response. Its control effect far exceeds that of traditional fixed-parameter PID controllers.

[0030] Preferably, the adjustment amount of the scaling factor is limited by a preset upper limit.

[0031] Preferably, the adjustment of the scaling factor is increased only when the corrected temperature deviation is not zero, and is dominated by the degree of spray influence.

[0032] The present invention has the following technical effects:

[0033] This invention no longer passively waits for the spraying operation to affect the temperature before compensating. Instead, it accurately models the spraying process physically, precisely quantifies the total heat removed, and introduces this as a feedforward signal into the control system. This control mode, which anticipates and actively counteracts disturbances, fundamentally decouples the strong interference of humidity regulation on temperature control, significantly improving the stability and control accuracy of the proofing chamber temperature.

[0034] Furthermore, a graded compensation mechanism for weak and strong disturbances was designed. For minor disturbances, an independent, non-intrusive heating system is used for compensation, avoiding interference with the main PID loop and ensuring the basic stability of the system. For severe disturbances, real-time, non-linear adaptive tuning of the PID parameters ensures a fast and powerful compensation effect. This intelligent and adaptive strategy selection enables the system to make optimal control responses under various disturbance intensities.

[0035] Furthermore, considering that spraying can cause uneven temperature distribution, an innovative deviation correction mechanism based on temperature gradients was introduced. This mechanism can dynamically adjust the system's perception of temperature deviation according to the actual temperature distribution inside the chamber, making the feedback input of the PID controller more reflective of the real temperature problems that need to be addressed first, thereby improving the control accuracy and adaptability to uneven temperature fields. Attached Figure Description

[0036] Figure 1 This is a flowchart of a method for intelligent temperature control of a dough proofing device provided in an embodiment of the present invention. Detailed Implementation

[0037] This invention discloses a method for intelligent temperature control in dough proofing equipment, referring to... Figure 1 This includes steps S1-S4:

[0038] S1: Collect temperature deviation and humidity data at various times.

[0039] Specifically, temperature sensors are installed at several locations within the proofing chamber to collect temperature data at each location in real time at each moment. The average temperature of all locations at each moment is calculated and recorded as the comprehensive temperature. The theoretical temperature at each moment is obtained, and the difference between the comprehensive temperature at each moment and the theoretical temperature is recorded as the temperature deviation.

[0040] A humidity sensor is installed inside the enclosure to collect humidity data in real time.

[0041] S2: In response to the spraying operation, the temperature deviation at each moment is corrected based on the temperature difference at different locations to obtain the corrected temperature deviation.

[0042] It should be noted that PID control algorithms are generally based on the deviation between the temperature and the set temperature for temperature regulation. Therefore, obtaining an accurate temperature deviation can enable more accurate temperature control.

[0043] It should be further noted that because the spray causes uneven temperature distribution within the proofing box, the temperature deviation calculated based on the average temperature is not accurate enough. Therefore, it is necessary to correct the temperature deviation based on the temperature differences at different locations.

[0044] Preferably, as an example, the temperature deviation at each time point is corrected based on the temperature differences at different locations to obtain the corrected temperature deviation, including:

[0045] The difference between the maximum and minimum temperatures at all locations is recorded as the temperature gradient at each time point.

[0046] The corrected temperature deviation satisfies the following relationship:

[0047]

[0048] in, Let D represent the temperature deviation at each time step, and K represent the temperature gradient at each time step. K represents the gradient correction coefficient, which is used to control the degree of correction. This indicates the corrected temperature deviation.

[0049] It is understandable that temperature deviation correction can reduce the impact of temperature non-uniformity on the accuracy of temperature deviation and improve the accuracy of temperature deviation calculation.

[0050] S3: Calculate the heat carried away by the spray.

[0051] It should be noted that adjusting the humidity of the proofing room via spray will affect the temperature of the proofing space. Temperature deviations will negatively impact the proofing results; therefore, to improve the proofing effect, it is necessary to minimize the influence of humidity adjustments on temperature.

[0052] It should be further explained that the spray will carry away some heat, thereby lowering the temperature. Therefore, in order to reduce the impact of humidity adjustment on temperature, it is necessary to accurately measure the heat carried away by the spray.

[0053] S30: Calculate the heat carried away by the spray.

[0054] It should be noted that the heat carried away by the spray mainly consists of the heat absorbed by the spray, the heat absorbed by the vaporization of the spray, and the heat absorbed by the air introduced by the spray. Therefore, the heat carried away by the spray will be measured from these three aspects below.

[0055] Preferably, as an example, calculating the heat carried away by the spray includes:

[0056]

[0057] in, This represents the heat carried away by the spray at each moment, and is used to characterize the total heat loss carried away by the spray at each moment. This represents the sensible heat loss carried away by the spray at each moment, used to characterize the heat loss directly carried away by the spray at each moment. This represents the latent heat loss carried away by the spray at each moment, used to characterize the heat loss carried away by the vaporization of the spray at each moment. This represents the convective loss generated by the spray at each moment, used to characterize the heat absorbed by the air brought in by the spray.

[0058] The above discussion concerns the sensible heat loss and latent heat loss carried away by the spray. The following section will explain the methods for determining the sensible heat loss and latent heat loss carried away by the spray.

[0059] First, let's discuss the sensible heat loss carried away by the spray.

[0060] It should be noted that the sensible heat loss carried away by the spray is mainly due to the heat transfer loss caused by the temperature difference between the proofing chamber and the spray. The heat transfer loss carried away by the spray is mainly related to the temperature difference and the specific heat capacity of the spray; therefore, the sensible heat loss carried away by the spray will be calculated based on these factors below.

[0061] The sensible heat loss of the spray satisfies the following relationship:

[0062]

[0063] in, Indicates the quality of the spray. This indicates the specific heat capacity of water. This represents the combined temperature at various times. Indicates the temperature of the spray. This indicates the sensible heat loss of the spray at each moment.

[0064] Understandable, This reflects the temperature difference between the spray temperature and the temperature inside the proofing room. This reflects the heat transferred to the spray through thermal conduction.

[0065] Then, the latent heat loss carried away by the spray is introduced.

[0066] It should be noted that the latent heat loss of the spray is mainly due to heat loss caused by water vaporization. Spray vaporization is related to the spray mass and the vaporization constant of water; the latent heat loss of the spray will be calculated based on these factors below.

[0067] The latent heat loss carried away by the spray satisfies the following relationship:

[0068]

[0069] in, Indicates the quality of the spray. This represents the latent heat of vaporization of water. This constant is temperature-dependent and can be obtained by consulting a latent heat of vaporization lookup table. This indicates the latent heat loss carried away by the spray.

[0070] Finally, we will discuss the convection losses caused by the spray.

[0071] It should be noted that the convection losses caused by the spray are mainly due to heat loss caused by the air introduced by the spray. The temperature difference between the air introduced by the spray and the temperature inside the proofing chamber leads to heat conduction, which in turn affects the temperature of the proofing chamber.

[0072] The convective losses generated by the spray satisfy the following relationship:

[0073]

[0074] in, Indicates the density of the outside air. Indicates the volume of external air brought in. This indicates the specific heat capacity of air. This indicates the temperature of the outside air at each moment. This represents the combined temperature at various times. This indicates the convective losses caused by the spray.

[0075] Understandable, This reflects the difference between the introduced air and the temperature of the proofing chamber. The quality of the air brought in, This indicates the amount of heat lost due to heat transfer from the incoming air.

[0076] S4: Control compensation based on the heat carried away by the spray.

[0077] It should be noted that in order to reduce the impact of heat carried away by the spray, compensation based on certain control strategies is required.

[0078] It should be further noted that when the spray has a minor impact on temperature, directly adjusting the PID control system for temperature compensation can easily lead to instability of the PID control system. Therefore, temperature compensation should only be performed by adjusting the PID control system when the spray has a significant impact on temperature.

[0079] Preferably, as an example, control compensation based on the heat carried away by the spray includes:

[0080] The heat carried away by the spray is normalized, and the normalized value is recorded as the degree of spray influence.

[0081] The degree of spray impact is compared with a preset impact threshold. If the degree of spray impact is less than the preset impact threshold, weak interference compensation is used. The weak interference compensation can be performed by other heating systems. If the degree of spray impact is not less than the impact threshold, strong interference compensation is performed. The strong interference compensation is performed by adjusting the PID system.

[0082] Understandably, when the impact of the spray is less than the preset threshold, it indicates a relatively small impact. In this case, directly adjusting the PID system could easily lead to instability in the entire temperature control system. The resulting temperature deviation could be greater than that caused by the spray. Therefore, when the spray impact is small, compensation is provided by other heating systems. However, since other heating systems lack feedback adjustment capabilities and their temperature compensation accuracy is low, when the spray impact is significant, further adjustments to the PID system for temperature compensation are necessary.

[0083] The above covers strong interference compensation and weak interference compensation. The specific operations of strong interference compensation and weak interference compensation are explained below.

[0084] First, we will introduce weak interference compensation.

[0085] (1) Calculate the heat compensation for weak disturbances:

[0086]

[0087] in, This indicates the heat carried away by the spray. This represents the compensation coefficient, used to control the compensation intensity. Here, t represents the attenuation coefficient, t represents the time interval from when the spray stops to the current moment, and e represents the natural constant. This indicates the replenishment of heat.

[0088] It is understandable that, through heat conduction, the temperature of the spray will increase over time, and the heat carried away by the spray will gradually decrease. Therefore, an attenuation coefficient is introduced to reflect the attenuation of the heat carried away by the spray.

[0089] (2) Use a separate heating system to provide heat to the proofing box. The calories.

[0090] Next, we will introduce strong interference compensation.

[0091] It should be noted that the PID algorithm includes proportional, integral, and derivative terms, each with a corresponding coefficient. In order to reduce the impact of heat loss carried away by the spray, the coefficient of each term needs to be adjusted to reduce the interference of heat carried away by the spray.

[0092] (1) Adjust the proportional coefficient of the PID algorithm by utilizing the degree of spray influence.

[0093] It should be noted that the proportional coefficient is mainly adjusted based on the current temperature deviation. If the amount of heat carried away by the spray at the current moment is greater, the proportional coefficient should be increased to compensate for the heat carried away by the spray.

[0094] The adjustment of the proportionality coefficient satisfies the following relationship:

[0095]

[0096] in, This represents the preset proportional coefficient of the PID algorithm. This represents the first sensitivity coefficient, used to control the degree of adjustment of the proportional coefficient. This represents the error sensitivity coefficient, used to control the degree to which the error affects the adjustment of the sensitivity coefficient. This indicates a preset threshold for the degree of influence. Indicates the degree of impact of the spray. This indicates the set upper limit value to prevent [the value from being set too high]. The setting is too large. This indicates a non-zero temperature difference, where the corrected temperature deviation is equal to 0. Setting it to 0 will result in a temperature deviation that is not equal to 0 after correction. Set to 1, This indicates the corrected temperature deviation. This represents the adjusted proportional coefficient of the PID algorithm.

[0097] Understandable, This is to limit the magnitude of the adjustment. The larger the value, the greater the impact of the spray exceeds the threshold, and the more heat the spray carries away. Therefore, the proportional coefficient needs to be adjusted to be larger to increase the temperature compensation. This reflects the temperature difference. The larger the temperature difference, the larger the corresponding proportional coefficient, thereby achieving greater temperature adjustment and better reducing the temperature difference.

[0098] (2) Adjust the integral coefficient of the PID algorithm by using the heat carried away by the spray.

[0099] It should be noted that the integral term coefficient of the PID algorithm mainly participates in temperature adjustment by acting on the accumulated error. The integral term of the PID algorithm reflects more the static temperature difference of the system. This static temperature difference can be some temperature difference existing in the system, such as sensor error. Large adjustments to the integral term may cause system oscillations. Since the heat carried away by the spray is not within the scope of static error, but it will cause error accumulation, if a traditional integral coefficient is used, overshoot can easily occur, leading to system instability.

[0100] The adjustment of the integral coefficients satisfies the following relationship:

[0101]

[0102] in, This represents the preset integral coefficient of the PID algorithm. This represents the second sensitivity coefficient, used to control the degree of adjustment of the integral coefficient. This represents the adjusted integral coefficient of the PID algorithm.

[0103] It is understandable that the greater the impact of the spray, the greater the temperature deviation caused by the spray, and the greater the cumulative error caused by the spray. This accumulation of non-static error can easily cause system oscillation when applied to temperature control. Therefore, the integral coefficient needs to be reduced to decrease the impact of the cumulative error caused by the spray.

[0104] (3) Adjust the differential coefficient of the PID algorithm by using the heat carried away by the spray.

[0105] It should be noted that the differential coefficient is mainly used to adjust the temperature based on the temperature deviation. It determines the future temperature changes more. If the amount of heat carried away by the spray at the current moment is greater, the differential coefficient should be increased to compensate for the temperature before the heat carried away by the spray has an impact.

[0106]

[0107] in, This represents the preset derivative coefficients of the PID algorithm. This represents the third sensitivity coefficient, used to control the degree of adjustment of the differential coefficient. This represents the differential sensitivity coefficient, used to control the degree to which the derivative adjusts the differential coefficient. Indicates the rate of change of temperature deviation. This represents the adjusted differential coefficients of the PID algorithm.

[0108] Understandable, The larger the coefficient, the greater the impact of the spray, the more heat the spray carries away, and the greater the impact of the spray on future temperatures. Therefore, the differential coefficient should be increased to compensate for the heat before it affects the temperature.

[0109] Temperature is controlled using a PID algorithm with adjusted coefficients.

[0110] This invention also discloses a temperature intelligent control system for dough proofing equipment, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a temperature intelligent control method for dough proofing equipment according to the present invention is implemented.

[0111] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0112] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (DRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (DRAM), high-bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device.

Claims

1. A method for intelligent temperature control of a dough proofing device, characterized in that, Including the following steps: a. When performing a spraying operation inside the proofing chamber, obtain a temperature gradient that characterizes the non-uniformity of temperature distribution inside the chamber; b. Based on the temperature gradient, the original temperature deviation calculated from the combined temperature inside the chamber and the theoretical temperature is corrected to obtain a corrected temperature deviation; c. Calculate the total heat removed by the spray operation, including: calculating the sensible heat loss caused by direct heat absorption by the spray, the latent heat loss caused by spray vaporization, and the convective loss caused by air introduced by the spray, and summing the three together; d. Based on the total heat removed by the spray and the corrected temperature deviation, perform a control compensation to counteract the effect of the spray on the temperature; The method for performing a control compensation includes: normalizing the total heat carried away by the spray to obtain a spray impact level; comparing the spray impact level with a preset impact level threshold to select whether to perform weak interference compensation or strong interference compensation. Weak interference compensation is achieved through a separate heating system that provides a compensating heat that is related to the total heat carried away by the spray and decays over time; Strong disturbance compensation is achieved by adjusting the parameters of a PID algorithm used for temperature control. The adjustments include: increasing the proportional coefficient of the PID algorithm based on the degree of spray influence and the corrected temperature deviation; decreasing the integral coefficient of the PID algorithm based on the degree of spray influence; and increasing the derivative coefficient of the PID algorithm based on the degree of spray influence and the rate of change of the corrected temperature deviation.

2. The intelligent temperature control method for a dough proofing device according to claim 1, characterized in that, The method to correct the original temperature deviation is to multiply the original temperature deviation by a correction factor that is proportional to the temperature gradient.

3. The intelligent temperature control method for a dough proofing device according to claim 1, characterized in that, Sensible heat loss is calculated based on the spray mass, the specific heat capacity of water, and the temperature difference between the overall temperature inside the chamber and the spray temperature; latent heat loss is calculated based on the spray mass and the latent heat of vaporization of water; convection loss is calculated based on the volume of external air brought in, air density, specific heat capacity of air, and the temperature difference between the external air temperature and the overall temperature inside the chamber.

4. The intelligent temperature control method for a dough proofing device according to claim 1, characterized in that, The adjustment amount of the scaling factor is limited by a preset upper limit.

5. The intelligent temperature control method for a dough proofing device according to claim 1, characterized in that, The adjustment of the scaling factor is only made when the corrected temperature deviation is not zero, and is dominated by the degree of spray influence.

6. A temperature intelligent control system for a dough proofing device, characterized in that, include: The device includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement a temperature intelligent control method for a dough proofing device according to any one of claims 1-5.

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