Intelligent control method applied to freeze dryer and freeze-drying system

Abstract

The invention discloses an intelligent control method applied to a freeze dryer and a freeze-drying system.The intelligent control method comprises the steps that initialization parameters of the freeze dryer are obtained, and a current temperature feedback value and a current vacuum degree feedback value are collected; wherein the initialization parameters comprise a temperature set value and a vacuum degree set value; judging whether the vacuum degree feedback value meets a preset condition or not; if yes, according to the vacuum degree feedback value and the vacuum degree set value, a temperature command value is calculated through a PI algorithm; if not, setting the temperature command value as the temperature set value; and calculating a temperature error according to the temperature command value and the temperature feedback value, and calculating a PWM duty ratio through a PID algorithm based on the temperature error so as to adjust the actual power output of a heater of the freeze dryer and realize temperature control. According to the closed-loop control strategy, the precision and stability of temperature adjustment are greatly improved, the more energy-saving operation effect is achieved, and the overall performance of the freeze dryer is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of freeze-dryer control, in particular to an intelligent control method applied to a freeze-dryer and a freeze-drying system. BACKGROUND

[0002] A freeze-dryer is a device that removes water from materials by sublimation in a low-temperature and vacuum environment, and is widely used in the fields of medicine, biological products and food. The key to the freeze-drying process is the effective control of temperature and vacuum degree. Temperature control is used to ensure that the material is sublimated under suitable heating conditions, and vacuum degree control is used to maintain a low-pressure environment to promote the rapid evaporation and discharge of water. Only when the temperature and vacuum environment are reasonably regulated can high-quality freeze-drying be achieved. The current control method of the freeze-dryer is to maintain a certain temperature range by starting and stopping the heating element, and to maintain a predetermined vacuum range by starting and stopping the full-power control of the vacuum pump. This control method is simple in structure and can meet the basic needs of the freeze-drying process, but it has certain deficiencies.

[0003] Specifically, this control method causes the heating element and the vacuum pump to start and stop frequently, resulting in high energy consumption and making it difficult to achieve energy-saving operation. At the same time, due to the rough control method, the regulation accuracy of temperature and vacuum degree is limited, making it difficult to meet the requirements of modern freeze-drying processes for high-precision and high-stability environmental parameters. SUMMARY

[0004] Therefore, the present application provides an intelligent control method applied to a freeze-dryer and a freeze-drying system, aiming to solve the problems of high energy consumption and limited temperature regulation accuracy of the existing freeze-dryer control method.

[0005] To solve the above problems, the present application adopts the following technical solutions:

[0006] In a first aspect, the present application provides an intelligent control method applied to a freeze-dryer, comprising:

[0007] obtaining initialization parameters of the freeze-dryer, and collecting current temperature feedback value and vacuum degree feedback value; wherein the initialization parameters include temperature setting value and vacuum degree setting value;

[0008] determining whether the vacuum degree feedback value meets a predetermined condition; if yes, calculating a temperature command value by a PI algorithm according to the vacuum degree feedback value and the vacuum degree setting value; if no, setting the temperature command value as the temperature setting value;

[0009] calculating a temperature error according to the temperature command value and the temperature feedback value, and calculating a PWM duty cycle by a PID algorithm based on the temperature error;

[0010] According to the PWM duty cycle, a PWM signal is generated and output to a power drive circuit of the freeze dryer, so that the power drive circuit adjusts the actual power output of a heater of the freeze dryer according to the PWM signal, and temperature control is realized.

[0011] Further, the preset condition includes: confirming that the current rotating speed of a BLDC motor connected with a vacuum pump of the freeze dryer has reached the highest rotating speed, and the vacuum degree feedback value continues to increase.

[0012] Further, if the condition is met, a temperature command value is calculated through a PI algorithm according to the vacuum degree feedback value and the vacuum degree setting value, including:

[0013] If the condition is met, the vacuum degree feedback value is recorded as Zmeas(t), the vacuum degree setting value is recorded as Zset, and a vacuum degree error is calculated according to the formula: Ze(t) = Zset-Zmeas(t).

[0014] The temperature command value is recorded as Tcmd(t), and the temperature command value is calculated according to the formula: Tcmd(t) = Tset-[Kp,vac*Ze(t)+Ki,vac∑Ze(t)], wherein Tset is a temperature setting value, Kp,vac is a preset vacuum degree proportional amplification coefficient, Ki,vac is a preset vacuum degree integral amplification coefficient, and ∑Ze(t) represents the cumulative sum of the vacuum degree error from the initial time to the current time.

[0015] If the condition is not met, the temperature command value is set as the temperature setting value, including:

[0016] If the condition is not met, the temperature command value is recorded as Tcmd(t), the temperature setting value is recorded as Tset, and Tcmd(t) = Tset.

[0017] Further, the temperature error is calculated according to the temperature command value and the temperature feedback value, including: the temperature feedback value is recorded as Tmeas(t), the temperature error is recorded as e(t), and the temperature error is calculated according to the formula: e(t) = Tcmd(t)-Tmeas(t).

[0018] Further, the PWM duty cycle is calculated through a PID algorithm based on the temperature error, including:

[0019] The PID controller output is denoted as U(t), and the PID controller output is calculated according to a formula: U(t) = Kp,temp*e(t) + Ki,temp∑e(t) + Kd,temp*[e(t) - e(t-1)], wherein Kp,temp is a preset temperature proportional amplification coefficient, Ki,temp is a preset temperature integral amplification coefficient, Kd,temp is a preset temperature differential amplification coefficient, e(t) is a temperature error at a current time, e(t-1) is a temperature error at a last sampling time, and ∑e(t) represents a cumulative sum of the temperature error from an initial time to the current time;

[0020] The PID controller output is converted into a PWM duty ratio through a preset linear mapping relationship.

[0021] Further, after the PWM signal is output to the power driving circuit of the freeze dryer, the method further includes:

[0022] A current rotating speed of a BLDC motor connected with a vacuum pump of the freeze dryer is acquired, and it is determined whether the vacuum degree error is greater than a preset vacuum degree error threshold value;

[0023] If the current rotating speed of the BLDC motor is greater than the preset vacuum degree error threshold value, the current rotating speed of the BLDC motor is switched to a preset initial rotating speed;

[0024] If the current rotating speed of the BLDC motor is not greater than the preset vacuum degree error threshold value, a vacuum degree PID control mode is switched to; an output rotating speed is calculated through a PID algorithm based on the vacuum degree error, and the output rotating speed is converted into a driving signal and output to the BLDC motor, so as to drive the BLDC motor to adjust the current rotating speed to the output rotating speed, and realize vacuum degree adjustment.

[0025] Further, the output rotating speed is calculated through the PID algorithm based on the vacuum degree error, and includes:

[0026] The output rotating speed is denoted as M(t), and the output rotating speed is calculated according to a formula: M(t) = Kp,vac*Ze(t) + Ki,vac∑Ze(t) + Kd,vac*[Ze(t) - Ze(t-1)] + ZFM(t), wherein Kp,vac is a preset vacuum degree proportional amplification coefficient, Ki,vac is a preset vacuum degree integral amplification coefficient, Kd,vac is a preset vacuum degree differential amplification coefficient, Ze(t) is a vacuum degree error at a current time, Ze(t-1) is a vacuum degree error at a last sampling time, ∑Ze(t) represents a cumulative sum of the vacuum degree error from an initial time to the current time, and ZFM(t) is a preset vacuum degree motor rotating speed feedforward value.

[0027] Furthermore, after switching to the vacuum degree PID control mode, before the step based on the vacuum degree error, the method further includes: if it is confirmed that the first control cycle of entering the vacuum degree PID control mode has been entered, the error accumulation sum used for integral control in the PID algorithm is initialized and assigned an initial integral value calculated based on the vacuum degree error and preset parameters.

[0028] Further, the power drive circuit includes resistors R1, R5, and R3, capacitor C5, an ACPL-W341-500E type opto-isolator U3, capacitor C4, resistor R2, Zener diodes D4 and D5, resistor R4 and capacitor C6, a YGF20N65T2 type MOSFET U2, and diode D2; wherein, the first pin of the opto-isolator U3 is connected to one end of resistor R1, one end of resistor R3, and one end of capacitor C5, and the third pin of the opto-isolator U3 is connected to one end of resistor R5, the other end of resistor R3, and the other end of capacitor C5, the other end of resistor R1 is connected to the 3V3 power supply terminal, and the other end of resistor R5 is connected to the PWM signal terminal. Pin 4 of U3 is grounded. Pin 5 of the opto-isolator U3 is connected to one end of resistor R2. Pin 6 of the opto-isolator U3 is connected to the 15V power supply terminal and one end of capacitor C4. The other end of capacitor C4 is grounded. The other end of resistor R2 is connected to the cathode of Zener diode D4, one end of resistor R4, one end of capacitor C6, and the gate of MOSFET U2. The anode of Zener diode D4 is connected to the anode of Zener diode D5. The cathode of Zener diode D5 is connected to the other end of resistor R4, the other end of capacitor C6, the source of MOSFET U2, and the ground terminal. The drain of MOSFET U2 is connected to the anode of diode D2 and the heater of the freeze dryer. The cathode of diode D2 is connected to the BUS+-110 terminal.

[0029] Secondly, the present invention also proposes a freeze-drying system, comprising a freeze dryer and a control system electrically connected to each other, wherein the control system includes:

[0030] Memory, used to store program instructions; and

[0031] A processor is configured to execute the program instructions to implement the steps of the intelligent control method applied to the freeze dryer as described above.

[0032] Beneficial Effects: This intelligent control method and freeze-drying system applied to freeze dryers achieves continuous adjustment of heater power by dynamically calculating the temperature command value and PWM duty cycle through real-time acquisition of temperature and vacuum feedback values. This avoids the frequent start-stop problems associated with traditional on-off switching control, effectively reducing energy consumption and equipment wear. Specifically, the temperature command value is adjusted in conjunction with the vacuum feedback value and a PI algorithm to ensure that temperature control is consistent with the vacuum state. The PWM duty cycle is calculated using a PID algorithm, which adjusts the heater power in real-time based on temperature error and its trend, achieving precise control of the heating process. Compared to traditional on-off control, this closed-loop control strategy not only significantly improves the accuracy and stability of temperature regulation but also achieves more energy-efficient operation, significantly enhancing the overall performance and reliability of the freeze dryer. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating the first embodiment of the intelligent control method for a freeze dryer according to the present invention.

[0035] Figure 2 This is a flowchart illustrating the second embodiment of the intelligent control method for a freeze dryer according to the present invention;

[0036] Figure 3 This is a flowchart illustrating the third embodiment of the intelligent control method for a freeze dryer according to the present invention.

[0037] Figure 4 This is a circuit diagram of the power drive circuit of the intelligent control method for a freeze dryer according to the present invention;

[0038] Figure 5 This is a flowchart illustrating the fourth embodiment of the intelligent control method for a freeze dryer according to the present invention.

[0039] Figure 6 This is a flowchart illustrating the fifth embodiment of the intelligent control method for a freeze dryer according to the present invention;

[0040] Figure 7 This is a schematic diagram of the control system in the hardware operating environment involved in the embodiments of the present invention.

[0041] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0044] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the term "and / or" throughout the text includes three solutions; taking A and / or B as an example, it includes technical solution A, technical solution B, and a technical solution that simultaneously satisfies A and B. Furthermore, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0045] It should be noted that PWM (Pulse Width Modulation) is a commonly used temperature control technique. This technique achieves precise temperature regulation by controlling the heating element's switching at high speed and adjusting the duty cycle (the ratio of high-level time to the cycle time) of the PWM signal to precisely control the average power of the heating element. At a duty cycle of 100%, the heating element operates at full power; at 50%, it operates at half power; and at 0%, it is turned off. PWM technology offers advantages such as low power loss of the switching elements, high control precision, fast response speed, and simple and low-cost implementation.

[0046] Please see Figure 1 The diagram shown is a flowchart of the first embodiment of the intelligent control method of the present invention applied to a freeze dryer.

[0047] In this embodiment, the intelligent control method applied to a freeze dryer includes:

[0048] Step S100: Obtain the initialization parameters of the freeze dryer, and collect the current temperature feedback value and vacuum degree feedback value.

[0049] Among them, the initialization parameters include the temperature setting value and the vacuum degree setting value.

[0050] Specifically, the initialization parameters are the key control target values preset before the freeze dryer starts the intelligent control mode, mainly including the temperature setting value and the vacuum degree setting value. Among them, the temperature setting value is the reference temperature expected to be reached or maintained during the freeze-drying process, which is used to guide the heating intensity of the heater and the temperature control strategy. The vacuum degree setting value is the reference value of the required vacuum environment during the freeze-drying process, which is used to ensure that the material is dried under a suitable low-pressure environment, thereby ensuring the efficiency and quality of freeze-drying.

[0051] Step S200: Determine whether the vacuum degree feedback value meets the preset conditions; if it meets, calculate the temperature command value through the PI algorithm according to the vacuum degree feedback value and the vacuum degree setting value; if it does not meet, set the temperature command value to the temperature setting value.

[0052] Specifically, the vacuum degree feedback value is the vacuum degree data of the cold trap of the freeze dryer measured in real time by a vacuum sensor, which is used to reflect the actual vacuum state of the freeze-drying working condition. The temperature feedback value is the temperature data of the cavity of the freeze dryer collected in real time by a temperature sensor, which is used to monitor the temperature change during the freeze-drying process. The PI algorithm (Proportional-Integral control algorithm) dynamically adjusts the temperature command value according to the difference between the vacuum degree feedback value and the vacuum degree setting value, so as to achieve the accuracy of temperature control and the smoothness of response. In this embodiment, the PI algorithm performs calculations every 1 second to be able to respond to the change of the vacuum degree in time and achieve real-time linkage control of temperature and vacuum degree.

[0053] Step S300: Calculate the temperature error according to the temperature command value and the temperature feedback value, and calculate the PWM duty cycle based on the temperature error through the PID algorithm.

[0054] Specifically, the difference between the temperature command value and the temperature feedback value is the temperature error. The PID algorithm (Proportional-Integral-Differential control algorithm) calculates the PWM duty cycle according to the temperature error and its change trend. The PWM duty cycle determines the on-off time ratio of the heater, thereby adjusting the heating power and achieving precise control of the temperature. In this embodiment, the PID algorithm performs calculations every 0.1 second to ensure the real-time and accuracy of temperature adjustment.

[0055] It should be noted that the PWM signal frequency is fixed and can be selected within the range of 100Hz to 20kHz. Currently, a fixed frequency of 10kHz is used. The resolution of the PWM pulse width adjustment can be adjusted between 0.01% and 1%, and the current pulse width adjustment accuracy is set to 0.1%. Using PWM temperature control technology can significantly improve the energy efficiency of the freeze dryer, reduce electromagnetic and mechanical noise, and improve overall operating efficiency.

[0056] Step S400: Generate a PWM signal according to the PWM duty cycle, and output the PWM signal to the power drive circuit of the freeze dryer so that the power drive circuit adjusts the actual power output of the heater of the freeze dryer according to the PWM signal to achieve temperature control.

[0057] Specifically, the power drive circuit performs chopping control on the rectified 110V DC bus (BUS+110V) based on the PWM signal. Chopping control involves rapidly switching the DC current on and off to adjust the input current of the heater, thereby achieving precise control of the heater's power. The frequency of this chopping control is unaffected by the AC cycle and can reach up to 20kHz, effectively reducing the impact on the power supply and ensuring a more stable current for the heater.

[0058] Compared with existing technologies, the intelligent control method for freeze dryers in this invention continuously adjusts the heater power by dynamically calculating the temperature command value and PWM duty cycle through real-time acquisition of temperature and vacuum feedback values. This avoids the frequent start-stop problems caused by traditional on / off switching control, effectively reducing energy consumption and equipment wear. Specifically, the temperature command value is adjusted in conjunction with the vacuum feedback value and a PI algorithm to ensure that temperature control is consistent with the vacuum state. The PWM duty cycle is calculated using a PID algorithm, which adjusts the heater power in real-time based on temperature error and its trend, achieving precise control of the heating process. Compared with traditional on / off control, this closed-loop control strategy not only significantly improves the accuracy and stability of temperature regulation but also achieves more energy-efficient operation, significantly improving the overall performance and reliability of the freeze dryer.

[0059] Please see Figure 2 The diagram shown is a flowchart illustrating a second embodiment of the intelligent control method for a freeze dryer according to the present invention. This embodiment provides a detailed description of the steps for obtaining the temperature command value, compared to other embodiments of the intelligent control method for a freeze dryer.

[0060] In this embodiment, step S200 includes:

[0061] Step S201: Determine whether the vacuum feedback value meets the preset conditions.

[0062] The preset conditions include: confirming that the current speed of the BLDC motor connected to the vacuum pump of the freeze dryer has reached the maximum speed, and that the vacuum feedback value continues to increase.

[0063] Specifically, BLDC (Brushless DC Motor) motors offer advantages over traditional brushed motors, including longer lifespan, higher efficiency, lower noise, and less maintenance. If the current speed of the vacuum pump (i.e., the current speed of the brushless DC motor driving the vacuum pump, both values ​​being essentially the same) has reached its maximum speed, and the vacuum feedback value continues to increase (i.e., the pressure continues to decrease), it indicates that the vacuum pump is at its maximum pumping capacity and the vacuum environment is continuously strengthening. However, while a high vacuum is required in freeze-drying processes to promote drying, excessively high vacuum levels may affect process stability and equipment safety. Therefore, it is necessary to reasonably control the vacuum level to avoid over-vacuuming and ensure the safe and stable operation of the freeze dryer. It is evident that if the preset conditions are met, the vacuum level needs to be appropriately reduced (i.e., the pressure increased) to ensure the safe and stable operation of the freeze dryer. At this time, the PI algorithm dynamically adjusts the temperature command value based on the vacuum error, adjusting in the direction of decreasing the temperature command value, thereby reducing heating power, lowering material temperature and evaporation rate, to achieve the purpose of appropriately reducing the vacuum level and increasing the pressure.

[0064] Step S202: If not satisfied, record the temperature command value as Tcmd(t), the temperature setting value as Tset, and Tcmd(t) = Tset.

[0065] Specifically, if the preset conditions are not met, it means that the vacuum pump has not reached its limit speed and / or the vacuum level has not continued to rise, and the freeze dryer is in a relatively stable state. At this time, the temperature command value will maintain the preset reference value (i.e., the temperature setting value) without making any additional adjustments, so as to reduce unnecessary intervention and maintain the smooth operation and normal functioning of the freeze drying process.

[0066] Step S2031: If satisfied, record the vacuum feedback value as Zmeas(t), the vacuum setting value as Zset, and the vacuum error as Ze(t). Calculate the vacuum error according to the formula: Ze(t) = Zset - Zmeas(t).

[0067] Specifically, vacuum error is the difference between the target value (vacuum setting value) and the actual value (vacuum feedback value) of vacuum degree, and it is one of the key data for temperature control and adjustment of freeze dryer.

[0068] Step S2032: Record the temperature command value as Tcmd(t), and calculate the temperature command value according to the formula: Tcmd(t)=Tset-[Kp,vac*Ze(t)+Ki,vac∑Ze(t)] .

[0069] Where, Tset is the temperature setting value, Kp,vac is the preset proportional amplification coefficient of the vacuum degree, Ki,vac is the preset integral amplification coefficient of the vacuum degree, and ∑Ze(t) represents the cumulative sum of the vacuum degree error from the initial moment to the current moment.

[0070] Specifically, the temperature command value is the target temperature actually executed by the freeze dryer chamber. The temperature setting value is the reference temperature that the freeze dryer chamber expects to reach. When the vacuum degree feedback value deviates from the vacuum degree setting value, the proportional term Kp,vac*Ze(t) immediately adjusts the temperature command value to quickly respond to the deviation. The integral term Ki,vac∑Ze(t) eliminates the long-term deviation and ensures the accuracy of the temperature command value when the freeze dryer is in a steady state. By subtracting these two terms, the temperature command value can be automatically adjusted according to the change of the vacuum degree, realizing the linkage control of temperature and vacuum degree.

[0071] The intelligent control method applied to the freeze dryer in the embodiment of the present invention introduces the vacuum degree feedback signal of the cold trap of the freeze dryer during the temperature control process to assist the PID control, thereby improving the response speed and control accuracy of the temperature control, achieving a higher energy-saving effect, and better meeting the requirements of the freeze drying process.

[0072] Please refer to Figure 3 As shown, it is a schematic flowchart of the third embodiment of the intelligent control method applied to the freeze dryer of the present invention. Compared with other embodiments of the intelligent control method applied to the freeze dryer, this embodiment details the steps of obtaining the PWM signal and adjusting the heating power based on the PWM signal.

[0073] In this embodiment, the step S300 includes:

[0074] Step S301: Denote the temperature feedback value as Tmeas(t) and the temperature error as e(t), and calculate the temperature error according to the formula: e(t) = Tcmd(t) - Tmeas(t).

[0075] Specifically, the temperature error is the key data for subsequent temperature adjustment. Accurately calculating the temperature error is the basis for the freeze dryer to achieve precise temperature control.

[0076] Step S302: Denote the output of the PID controller as U(t), and calculate the output of the PID controller according to the formula: U(t) = Kp,temp*e(t) + Ki,temp∑e(t) + Kd,temp*[e(t) - e(t - 1)].

[0077] Wherein, Kp,temp is the preset temperature proportional amplification factor, Ki,temp is the preset temperature integral amplification factor, Kd,temp is the preset temperature differential amplification factor, e(t) is the temperature error at the current moment, e(t-1) is the temperature error at the previous sampling moment, and ∑e(t) represents the cumulative summation of the temperature error from the initial moment to the current moment.

[0078] Specifically, the PID controller achieves precise temperature control by comprehensively considering the current temperature error, the accumulated temperature error, and the rate of change of the temperature error. This control strategy can quickly respond to temperature deviations, eliminate persistent deviations in a steady state, and suppress temperature fluctuations and over-adjustment, thereby improving the stability and response speed of temperature control.

[0079] Step S303: Convert the PID controller output into PWM duty cycle through a preset linear mapping relationship.

[0080] Specifically, the PID controller output U(t) is a continuous control quantity that typically varies within a certain range. By pre-setting a linear mapping relationship, the PID controller output is converted into a corresponding PWM duty cycle, ensuring that the maximum value of the PID controller output corresponds to a 100% duty cycle and the minimum value corresponds to a 0% duty cycle, thereby preventing signal overflow or failure. This linear mapping method is simple in structure and easy to implement, ensuring the continuity and consistency between the control signal and the execution signal, enabling the power drive circuit to accurately control the heater's output power and achieve precise temperature regulation.

[0081] The intelligent control method for freeze dryers described in this invention calculates the PWM duty cycle using a PID algorithm based on the difference between the temperature setpoint and the temperature feedback value, and outputs the corresponding PWM signal to the power drive circuit to achieve precise temperature control. This intelligent control method can effectively shorten the overall freeze-drying process time and improve production efficiency.

[0082] Please see Figure 4 The diagram shows the circuit schematic of the power drive circuit of the intelligent control method of the present invention applied to a freeze dryer.

[0083] In some embodiments of this application, the power drive circuit includes resistors R1, R5, R3, capacitor C5, an ACPL-W341-500E type opto-isolator U3, capacitor C4, resistor R2, Zener diode D4, Zener diode D5, resistor R4, capacitor C6, a YGF20N65T2 type MOSFET U2, and diode D2; wherein, the first pin of the opto-isolator U3 is connected to one end of resistor R1, one end of resistor R3, and one end of capacitor C5, the third pin of the opto-isolator U3 is connected to one end of resistor R5, the other end of resistor R3, and the other end of capacitor C5, the other end of resistor R1 is connected to the 3V3 power supply terminal, and the other end of resistor R5 is connected to the PWM signal terminal. Pin 4 of the electrical isolator U3 is grounded. Pin 5 of the opto-isolator U3 is connected to one end of resistor R2. Pin 6 of the opto-isolator U3 is connected to the 15V power supply terminal and one end of capacitor C4. The other end of capacitor C4 is grounded. The other end of resistor R2 is connected to the cathode of Zener diode D4, one end of resistor R4, one end of capacitor C6, and the gate of MOSFET U2. The anode of Zener diode D4 is connected to the anode of Zener diode D5. The cathode of Zener diode D5 is connected to the other end of resistor R4, the other end of capacitor C6, the source of MOSFET U2, and the ground terminal. The drain of MOSFET U2 is connected to the anode of diode D2 and the heater of the freeze dryer. The cathode of diode D2 is connected to the BUS+-110 terminal.

[0084] Specifically, opto-isolator U3 safely isolates the rectified high-voltage 110V DC bus from the low-voltage control circuit, preventing damage from the high voltage and reducing interference. MOSFET U2 features fast switching speed, low on-resistance, and high efficiency, making it suitable for high-frequency PWM control. This power drive circuit uses opto-isolator U3 to achieve safe isolation between the drive signal and the high-voltage power side, and then uses MOSFET U2 to perform PWM chopping control on the rectified DC bus, thereby achieving efficient and precise power regulation to meet the temperature control requirements of the freeze-drying process.

[0085] Please see Figure 5 The diagram shown is a flowchart of the fourth embodiment of the intelligent control method for a freeze dryer according to the present invention. This embodiment, based on the first embodiment of the intelligent control method for a freeze dryer, adds a step of dynamically adjusting the speed of the BLDC motor based on changes in vacuum level to achieve precise vacuum level adjustment.

[0086] In this embodiment, after step S400, the method further includes:

[0087] Step S501: Obtain the current rotation speed of the BLDC motor connected to the vacuum pump of the freeze dryer, and determine whether the vacuum error is greater than the preset vacuum error threshold.

[0088] Specifically, the current speed of the BLDC (Brushless DC Motor) refers to the real-time operating speed of the brushless DC motor connected to the vacuum pump, typically measured in revolutions per minute (RPM). The BLDC motor's speed information can be acquired in real time through feedback signals from its built-in sensors (such as Hall effect sensors or encoders) or the controller. The minimum speed of the BLDC motor can be set to 100 RPM, and the maximum speed can be adjusted to 5000 RPM. In this embodiment, the preset vacuum error threshold is 10 Pa. This value is determined based on the freeze dryer's process requirements and actual operating experience, ensuring both rapid response of the control system and avoiding instability caused by frequent speed switching of the BLDC motor, thus achieving efficient and stable vacuum control.

[0089] Step S502: If the error exceeds the preset vacuum level threshold, the current speed of the BLDC motor is switched to the preset initial speed.

[0090] Specifically, if the vacuum error is large, it indicates that the vacuum feedback value deviates significantly from the set vacuum value. In this case, PID control is not suitable. The BLDC motor will operate at a fixed preset initial speed, thereby ensuring that the vacuum pump can quickly reduce the vacuum error with a stable and high pumping capacity, avoiding instability in the BLDC motor's operation caused by frequent speed adjustments.

[0091] Step S503: If the error is not greater than the preset vacuum error threshold, switch to vacuum PID control mode; based on the vacuum error, calculate the output speed through PID algorithm, and convert the output speed into a drive signal to output to the BLDC motor, so as to drive the BLDC motor to adjust the current speed to the output speed, thereby realizing vacuum regulation.

[0092] Specifically, if the vacuum error is small (not greater than, i.e., less than or equal to), the closed-loop regulation advantage of PID control can be utilized to achieve precise control and stable maintenance of the vacuum level. In this embodiment, the PID algorithm is set to perform calculations every 0.1 seconds to ensure timely and smooth response. It should be noted that the drive signal is a PWM signal. By adjusting the duty cycle of the PWM, the drive voltage of the BLDC motor is changed, thereby adjusting the current speed of the BLDC motor.

[0093] Steps S502 and S503 embody different speed control strategies for BLDC motors. There is no specific order between them; either one can be implemented.

[0094] The intelligent control method for freeze dryers in this invention utilizes the adjustable speed of a BLDC motor to drive a compressor (i.e., a vacuum pump), thereby achieving rapid adjustment and high-precision control of the compressor's vacuum level.

[0095] Please see Figure 6 The diagram shown is a flowchart of the fifth embodiment of the intelligent control method for a freeze dryer according to the present invention. This embodiment, compared to other embodiments of the intelligent control method for a freeze dryer, provides a detailed description of the steps for obtaining the output rotational speed and adjusting the vacuum level based on the output rotational speed.

[0096] In this embodiment, step S503 includes:

[0097] Step S5031: If the error is not greater than the preset vacuum degree error threshold, switch to vacuum degree PID control mode.

[0098] Specifically, the vacuum degree PID control mode is a closed-loop feedback control method that can dynamically adjust the speed of the BLDC motor according to the magnitude, cumulative value and rate of change of the vacuum degree error, so as to achieve fine adjustment.

[0099] Step S5032: If it is confirmed that the first control cycle of the vacuum degree PID control mode has been entered, the error accumulation sum used for integral control in the PID algorithm is initialized and assigned the value as the initial integral value calculated based on the vacuum degree error and preset parameters.

[0100] Specifically, when a BLDC motor switches from a fixed preset initial speed mode to a vacuum degree PID control mode, the motor speed needs to smoothly transition from the preset initial speed to the output speed calculated by the PID algorithm. At this point, directly using the previously accumulated integral error in the PID algorithm could lead to abrupt changes in the integral term, causing speed jumps or oscillations and affecting the BLDC motor's operational stability. Therefore, in the first control cycle of entering the vacuum degree PID control mode, the accumulated integral error needs to be initialized with a reasonable initial integral value. This initial integral value is calculated based on the current vacuum degree error and preset parameters, aiming to ensure a smooth transition between the current speed and the output speed. By initializing the integral term, it is ensured that the output speed in the first cycle of vacuum degree PID control will not experience abrupt changes, avoiding drastic speed fluctuations and improving the dynamic response performance and stability of the BLDC motor.

[0101] Step S5033: Let the output speed be M(t). Calculate the output speed according to the formula: M(t)=Kp,vac*Ze(t)+Ki,vac∑Ze(t)+Kd,vac*[Ze(t)-Ze(t-1)]+ZFM(t).

[0102] Wherein, Kp,vac is the preset vacuum degree proportional amplification factor, Ki,vac is the preset vacuum degree integral amplification factor, Kd,vac is the preset vacuum degree differential amplification factor, Ze(t) is the vacuum degree error at the current moment, Ze(t-1) is the vacuum degree error at the previous sampling moment, ∑Ze(t) represents the cumulative summation of the vacuum degree error from the initial moment to the current moment, and ZFM(t) is the preset vacuum degree motor speed feedforward value.

[0103] Specifically, this PID calculation formula fully leverages the advantages of proportional, integral, and derivative terms to achieve comprehensive adjustment of vacuum level error, thereby ensuring precise vacuum level control. The introduced feedforward term (preset vacuum level motor speed feedforward value) is derived from experimental testing and can compensate for load changes in the control system in advance, reducing errors and thus improving the response accuracy of vacuum level PID control.

[0104] Step S5034: Convert the output speed into a drive signal and output it to the BLDC motor so as to drive the BLDC motor to adjust the current speed to the output speed, thereby realizing vacuum regulation.

[0105] Specifically, the converted drive signal is output to the BLDC motor driver in the form of a PWM duty cycle. The driver then adjusts the current and magnetic field of the BLDC motor according to this signal, thereby changing the BLDC motor speed. By adjusting the speed, the BLDC motor can change the operating state of the vacuum pump, thus regulating the vacuum level. By continuously calculating the output speed and converting it into a PWM duty cycle drive signal, a closed-loop control circuit is formed. This allows for real-time response to vacuum level errors, dynamically adjusting the motor speed to ensure the vacuum level remains stable within a reasonable range.

[0106] The intelligent control method for freeze dryers in this invention initializes the integral error accumulation sum during the first control cycle of the vacuum PID control mode to ensure smooth switching of the BLDC motor speed. This operation effectively avoids the abrupt changes caused by the integral term, achieving a smooth transition from a fixed preset initial speed to the PID-calculated output speed, thus improving the stability and control accuracy of vacuum regulation.

[0107] The following are embodiments of the freeze-drying system provided by the present invention. The embodiments of the freeze-drying system and the embodiments of the intelligent control method applied to a freeze dryer described above belong to the same concept. Details not fully described in the embodiments of the freeze-drying system can be found in the embodiments of the intelligent control method applied to a freeze dryer described above.

[0108] In this embodiment, a freeze-drying system includes a freeze dryer and a control system electrically connected to each other. The control system includes:

[0109] Memory 1005 is used to store program instructions; and

[0110] The processor 1001 is used to execute the program instructions to implement the steps of the intelligent control method applied to the freeze dryer as described above.

[0111] Please see Figure 7 As shown, it is a schematic diagram of the structure of the control system in the hardware operating environment involved in the embodiment of the present invention.

[0112] The control system in this embodiment of the invention can be a computing device such as a desktop computer, laptop, handheld computer, or server. Figure 7 As shown, the control system may include: a processor 1001 (e.g., CPU), a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit, such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0113] Those skilled in the art will understand that Figure 7 The control system structure shown does not constitute a limitation on the control system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0114] like Figure 7 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and computer programs.

[0115] exist Figure 7 In the control system shown, the network interface 1004 is mainly used to connect to the backend server and communicate with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate with the client; and the processor 1001 can be used to call the computer program stored in the memory 1005. When the computer program is called and executed by the processor 1001, it implements the steps of the above-mentioned intelligent control method applied to the freeze dryer.

[0116] The freeze-drying system of the present invention, since it can implement the steps of the above-described intelligent control method for freeze dryers, has at least all the beneficial effects brought about by the technical solutions of the above-described intelligent control method for freeze dryers, which will not be elaborated here.

[0117] The above description is only a part or preferred embodiment of the present invention. Neither the text nor the drawings should limit the scope of protection of the present invention. All equivalent structural transformations made using the content of the present invention specification and drawings under the overall concept of the present invention, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process.Figure 1 One or more processes and / or boxes Figure 1 Figure 1 The steps of the function specified in one or more boxes.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent control method for a freeze dryer, characterized in that, include: The initialization parameters of the freeze dryer are obtained, and the current temperature feedback value and vacuum feedback value are collected; wherein, the initialization parameters include temperature setting value and vacuum setting value; Determine whether the vacuum feedback value meets the preset conditions; if it does, calculate the temperature command value using the PI algorithm based on the vacuum feedback value and the vacuum setting value; if it does not meet the conditions, set the temperature command value to the temperature setting value. The temperature error is calculated based on the temperature command value and the temperature feedback value, and the PWM duty cycle is calculated based on the temperature error using a PID algorithm. A PWM signal is generated based on the PWM duty cycle, and the PWM signal is output to the power drive circuit of the freeze dryer so that the power drive circuit adjusts the actual power output of the heater of the freeze dryer according to the PWM signal to achieve temperature control.

2. The intelligent control method for a freeze dryer according to claim 1, characterized in that, The preset conditions include: confirming that the current speed of the BLDC motor connected to the vacuum pump of the freeze dryer has reached the maximum speed, and that the vacuum feedback value continues to increase.

3. The intelligent control method for a freeze dryer according to claim 1, characterized in that: If the conditions are met, then based on the vacuum feedback value and the vacuum setting value, the temperature command value is calculated using a PI algorithm, including: If satisfied, the vacuum feedback value is recorded as Zmeas(t), the vacuum setting value is Zset, and the vacuum error is Ze(t). The vacuum error is calculated according to the formula: Ze(t) = Zset - Zmeas(t). The temperature command value is denoted as Tcmd(t). The temperature command value is calculated using the formula: Tcmd(t) = Tset - [Kp,vac*Ze(t) + Ki,vac∑Ze(t)], where Tset is the temperature setting value, Kp,vac is the preset vacuum degree proportional amplification factor, Ki,vac is the preset vacuum degree integral amplification factor, and ∑Ze(t) represents the cumulative summation of the vacuum degree error from the initial time to the current time. If the condition is not met, the temperature command value will be set to the temperature setting value, including: If the conditions are not met, the temperature command value is recorded as Tcmd(t), the temperature setting value is recorded as Tset, and Tcmd(t) = Tset.

4. The intelligent control method for a freeze dryer according to claim 3, characterized in that, The step of calculating the temperature error based on the temperature command value and the temperature feedback value includes: denoting the temperature feedback value as Tmeas(t) and the temperature error as e(t), and calculating the temperature error according to the formula: e(t) = Tcmd(t) - Tmeas(t).

5. The intelligent control method for a freeze dryer according to claim 4, characterized in that, The calculation of the PWM duty cycle based on the temperature error using a PID algorithm includes: Let the output of the PID controller be U(t). Calculate the PID controller output according to the formula: U(t)=Kp,temp*e(t)+Ki,temp∑e(t)+Kd,temp*[e(t)-e(t-1)]; where Kp,temp is the preset temperature proportional amplification factor, Ki,temp is the preset temperature integral amplification factor, Kd,temp is the preset temperature differential amplification factor, e(t) is the temperature error at the current moment, e(t-1) is the temperature error at the previous sampling moment, and ∑e(t) represents the cumulative summation of the temperature error from the initial moment to the current moment. The output of the PID controller is converted into a PWM duty cycle through a preset linear mapping relationship.

6. The intelligent control method for a freeze dryer according to claim 3, characterized in that, After outputting the PWM signal to the power drive circuit of the freeze dryer, the system further includes: Obtain the current rotational speed of the BLDC motor connected to the vacuum pump of the freeze dryer, and determine whether the vacuum error is greater than a preset vacuum error threshold. If the error exceeds the preset vacuum level threshold, the current speed of the BLDC motor will be switched to the preset initial speed. If the error is not greater than the preset vacuum error threshold, the system switches to vacuum PID control mode. Based on the vacuum error, the system calculates the output speed using a PID algorithm and converts the output speed into a drive signal to be output to the BLDC motor. This drives the BLDC motor to adjust the current speed to the output speed, thereby achieving vacuum regulation.

7. The intelligent control method for a freeze dryer according to claim 6, characterized in that, The calculation of the output rotational speed based on the vacuum error using a PID algorithm includes: Let the output speed be M(t). Calculate the output speed according to the formula: M(t)=Kp,vac*Ze(t)+Ki,vac∑Ze(t)+Kd,vac*[Ze(t)-Ze(t-1)]+ZFM(t); where Kp,vac is the preset vacuum degree proportional amplification factor, Ki,vac is the preset vacuum degree integral amplification factor, Kd,vac is the preset vacuum degree differential amplification factor, Ze(t) is the vacuum degree error at the current moment, Ze(t-1) is the vacuum degree error at the previous sampling moment, ∑Ze(t) represents the cumulative summation of the vacuum degree error from the initial moment to the current moment, and ZFM(t) is the preset vacuum degree motor speed feedforward value.

8. The intelligent control method for a freeze dryer according to claim 6, characterized in that, After switching to the vacuum degree PID control mode, before the step based on the vacuum degree error, the method further includes: if it is confirmed that the first control cycle of the vacuum degree PID control mode has been entered, the error accumulation sum used for integral control in the PID algorithm is initialized and assigned an initial integral value calculated based on the vacuum degree error and preset parameters.

9. The intelligent control method for a freeze dryer according to claim 1, characterized in that, The power drive circuit includes resistors R1, R5, and R3, capacitor C5, an ACPL-W341-500E type opto-isolator U3, capacitor C4, resistor R2, Zener diodes D4 and D5, resistor R4, capacitor C6, a YGF20N65T2 type MOSFET U2, and diode D2. The first pin of the opto-isolator U3 is connected to one end of resistor R1, one end of resistor R3, and one end of capacitor C5. The third pin of the opto-isolator U3 is connected to one end of resistor R5, the other end of resistor R3, and the other end of capacitor C5. The other end of resistor R1 is connected to the 3V power supply terminal, and the other end of resistor R5 is connected to the PWM signal terminal. Pin 4 of the opto-isolator U3 is grounded. Pin 5 of the opto-isolator U3 is connected to one end of resistor R2. Pin 6 of the opto-isolator U3 is connected to the 15V power supply terminal and one end of capacitor C4. The other end of capacitor C4 is grounded. The other end of resistor R2 is connected to the negative terminal of Zener diode D4, one end of resistor R4, one end of capacitor C6, and the gate of MOSFET U2. The positive terminal of Zener diode D4 is connected to the positive terminal of Zener diode D5. The negative terminal of Zener diode D5 is connected to the other end of resistor R4, the other end of capacitor C6, the source of MOSFET U2, and the ground terminal. The drain of MOSFET U2 is connected to the positive terminal of diode D2 and the heater of the freeze dryer. The negative terminal of diode D2 is connected to the BUS+-110 terminal.

10. A freeze-drying system comprising a freeze dryer and a control system electrically connected to each other, characterized in that, The control system includes: Memory, used to store program instructions; and A processor for executing the program instructions to implement the steps of the intelligent control method for a freeze dryer as described in any one of claims 1 to 9.

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