Ultra-low temperature refrigerator self-adaptive temperature control method based on fuzzy control

Abstract

The invention discloses an ultra-low temperature refrigerator self-adaptive temperature control method based on fuzzy control, and relates to the technical field of intelligent control and industrial automation. Initializing target temperature, pressure and flow, acquiring data by using a high-precision sensor, and performing Kalman filtering to reduce noise; a fuzzy control rule base is constructed, the temperature deviation and the change rate serve as input, and the refrigerating / heating power adjusting quantity is deduced and output; a compressor, a heating pipe and an expansion valve are adjusted according to the reasoning result, and fine adjustment is switched when the deviation is smaller than 0.5 DEG C; dynamically adjusting the pressure to avoid boiling or cavitation of the secondary refrigerant, monitoring the liquid level of the constant-pressure tank and supplementing liquid; an interference compensation model is established, the system state is monitored in real time, faults are recorded, and pharmacy, military industry and other scenes are adapted. The temperature control precision and stability of the ultra-low temperature refrigerator are improved, pressure-temperature cooperative control is achieved, the interference influence is offset, it is guaranteed that a secondary refrigerant is stable, multi-load and fault switching are adapted, the high-precision requirements of pharmacy and the military industry are met, and the shutdown risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent control and industrial automation technology, and in particular to an adaptive temperature control method for ultra-low temperature freezers based on fuzzy control. Background Technology

[0002] In industrial production, cryogenic freezers are key supporting equipment in high-precision industries such as pharmaceuticals, military, and semiconductors. Their temperature control performance directly affects the stability of production processes and product quality. As these industries increasingly demand higher precision in temperature control, pressure stability, and adaptability to different operating conditions, traditional cryogenic freezers are gradually revealing numerous technical bottlenecks. Looking at the current state of technology both domestically and internationally, temperature control systems from countries like the US, Japan, and Germany, with their intelligent algorithms and high-precision hardware, offer superior control performance under complex operating conditions. These systems often employ advanced algorithms such as fuzzy control and adaptive control, enabling them to adapt to temperature systems with high inertia and nonlinearity, and possessing the ability to coordinate pressure and temperature regulation. Their products dominate the global high-end market. In contrast, domestic cryogenic freezer technology lags behind, with most still relying on conventional PID control. Parameter adjustment heavily depends on manual experience and on-site debugging, making it difficult to handle complex scenarios such as time-varying hysteresis and fluctuating heat loads. Especially under cryogenic and high-pressure conditions, temperature control accuracy is prone to deviating from target values, and the risk of pressure runaway is high.

[0003] Existing cryogenic freezers face multiple challenges in practical applications: First, insufficient temperature and pressure coupling control. The boiling point and freezing point of refrigerants such as 50% ethylene glycol are strongly correlated with pressure. Traditional systems lack dynamic coupling adjustment mechanisms. At high temperatures, insufficient pressure can easily cause the refrigerant to boil, and at low temperatures, excessively low pressure can easily cause cavitation, which not only affects heat exchange efficiency but may also damage supporting equipment such as reactors and distillation equipment, posing safety hazards. Second, weak anti-interference capability. Fluctuations in ambient temperature and changes in load heat load can easily lead to increased temperature control deviations. Traditional systems have a single compensation mechanism and cannot quickly respond to dynamic interference. Third, insufficient adaptability and stability. The load requirements of different industries vary greatly. Traditional systems lack targeted adjustment strategies, and problems such as sensor failure and changes in refrigerant concentration can easily lead to system shutdown, failing to meet the needs of continuous industrial production.

[0004] From an industry demand perspective, the pharmaceutical industry, as one of the core application areas for ultra-low temperature freezers, is experiencing increasingly stringent requirements for the accuracy and stability of reactor temperature control as the market continues to expand. The military industry, with its heat sink experiments and component environmental simulation testing, demands that ultra-low temperature freezers operate reliably for extended periods under extreme conditions. The rapid development of the semiconductor industry also places higher demands on the heat exchange efficiency and response speed of supporting temperature control equipment. However, current domestic ultra-low temperature freezer technology is insufficient to fully meet these demands. There is an urgent need for an adaptive temperature control method that integrates advanced control algorithms, pressure-temperature coordinated regulation, and dynamic interference compensation to address the pain points of traditional technologies, improve the control accuracy, stability, and industry adaptability of ultra-low temperature freezers, and drive the upgrading of domestic industrial temperature control equipment towards high-end applications. Summary of the Invention

[0005] The present invention proposes an adaptive temperature control method for ultra-low temperature freezers based on fuzzy control to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Step S1, System Initialization and Real-time Parameter Acquisition: Start the system and initialize the core temperature control parameters. Deploy multiple types of sensors to collect real-time temperature, pressure, and flow data. The collected data is transmitted to the central controller via the bus. The Kalman filter algorithm is used for noise reduction, and outliers exceeding the normal range of ±3σ are removed.

[0008] Step S2, Fuzzy Control Rule Base Construction and Inference: Temperature deviation ΔT and temperature change rate dT / dt are used as input variables, and cooling power adjustment ΔP and heating power adjustment ΔQ are used as output variables. Fuzzy subsets are divided for each variable, and a trapezoidal membership function is constructed. Fuzzy rules are established based on industrial experience and experimental data. Fuzzy inference is performed using the Mamdani inference method. The output fuzzy set is obtained through the maximum-minimum synthesis rule, and then the centroid method is used to defuzzify the data, outputting directly executable ΔP and ΔQ.

[0009] Step S3, Adaptive Temperature Regulation: The central controller adjusts the actuator based on the fuzzy inference results; it adjusts the frequency of the scroll compressor by ΔP; it adjusts the power of the electric heating element by ΔQ; and it adjusts the opening of the electronic expansion valve based on the temperature deviation. When the actual temperature is higher than the target and ΔP is negative, the opening increases, and vice versa. When |ΔT| < 0.5℃, it enters the fine adjustment mode, where the compressor frequency adjustment step size is reduced to 0.5Hz, the heating power step size is reduced to 5%, and the expansion valve opening step size is reduced to 1%.

[0010] Step S4, Pressure-Temperature Coordinated Control: Dynamically adjust the system pressure based on the temperature-pressure coupling characteristics of the 50% ethylene glycol refrigerant; when T≥100℃, start the pressurization process, pressurizing at a rate of 0.1 bar / min until the boiling point-pressure curve requirements are met; when T≤-35℃, start the pressure holding process, maintaining the pipeline pressure at 0.8~1.2 bar; when P≥4.5 bar, trigger the back pressure valve to release pressure and shut off the heating; monitor the liquid level in the constant pressure tank through an ultrasonic sensor, and automatically replenish the liquid when it is below 1 / 3.

[0011] This invention also includes:

[0012] Step S5, Dynamic Interference Compensation and Status Monitoring: Dynamically compensate power based on ambient temperature fluctuations, adjusting cooling power accordingly for every 5°C change in ambient temperature; calculate real-time heat load using flow rate and temperature difference, increasing cooling power by 1.2kW or decreasing heating power by 1.2kW for every 1kW increase in heat load; monitor system status in real-time, triggering a Level 1 warning if temperature deviation exceeds ±1°C for 5 seconds, increasing the adjustment range by 20%; triggering a Level 2 warning if pressure exceeds 5 bar or falls below 0.3 bar for 3 seconds, immediately shutting down the compressor and heating element and releasing pressure; triggering a Level 3 warning if flow rate falls below 0.3L / min for 5 seconds, stopping the machine and alarming for blockage, while recording fault information.

[0013] Step S6: Accumulated Temperature Deviation Prediction and Anticipatory Adjustment. This step captures the cumulative trend of temperature deviation through integral calculations, adjusting temperature control parameters in advance to avoid overshoot caused by the lag in traditional temperature control. It is particularly suitable for scenarios with slowly changing heat loads. The calculation method for the accumulated temperature deviation is as follows: , where T acc Let ΔT(τ) be the cumulative temperature deviation at time t, and ΔT(τ) be the instantaneous temperature deviation at time τ. ΔT(τ) = target temperature - T(τ), where t is the monitoring time period and τ is the integral variable. acc When the temperature exceeds 10℃•s, it is determined that the temperature is consistently too low. Therefore, the heating power adjustment ΔQ is increased by 15% in advance, while the cooling power adjustment ΔP is decreased by 15%. When T... acc When the temperature is less than -10℃•s, it is determined that the temperature is consistently too high. Therefore, the cooling power adjustment ΔP is increased by 15% in advance, while the heating power adjustment ΔQ is decreased by 15% to suppress the temperature rise. When |T acc When |≤10℃•s, maintain the original fuzzy inference result; after adjustment, recalculate T every 5s. acc Dynamically adjust the lead time.

[0014] This invention also includes:

[0015] Step S7: Precise adjustment of pressure-temperature coupling. Considering the nonlinear coupling relationship between refrigerant temperature and pressure, the target pressure is dynamically set based on the integral of temperature deviation. The formula for setting the target pressure is as follows:

[0016] Among them, P set Let P0 be the current target pressure, P0 be the base pressure, k be the pressure-temperature coupling coefficient, and T be the temperature. set Let T(τ) be the target temperature, T(τ) be the actual temperature at time τ, t be the settling time, and τ be the integral variable.

[0017] This invention also includes:

[0018] Step S8: Refrigerant flow adaptive matching. Based on real-time temperature deviation and pressure status, the circulation flow rate is dynamically adjusted to optimize heat exchange efficiency, avoiding excessive energy consumption due to excessive flow rate or insufficient heat exchange due to insufficient flow rate. First, the flow rate adjustment coefficient K is calculated. f K f =0.5+0.1×|ΔT(τ)|+0.05×(PP set ), where ΔT(τ) is the temperature deviation and P is the actual pressure, P set The target pressure; then based on the basic flow rate Q base Calculate the target flow Q set =Q base ×K f Flow control is achieved through a variable frequency circulating pump, and the relationship between speed and flow rate is n=n base ×(Q set / Q base ), where n is the target rotational speed, n base The base rotational speed; when |ΔT(τ)|>2℃ and K f When <1.2, force K to be f Increase to 1.2 to accelerate heat exchange; when P>P set +0.5 bar and K f When K > 0.8, force K to be... f Reduce the flow rate to 0.8 to lower the pipeline pressure; after flow adjustment, verify the flow rate through sensor feedback. If the deviation exceeds ±0.2L / min, adjust the speed step by 50rpm until the standard is met.

[0019] This invention also includes:

[0020] Step S9: Refrigerant Concentration Monitoring and Automatic Compensation. To address concentration changes caused by evaporation and leakage during long-term circulation of the 50% ethylene glycol solution, online monitoring and compensation are used to maintain concentration stability and prevent a rise in freezing point or a decrease in boiling point. An online refractometer is installed in the refrigerant return line, and the concentration C is measured every 30 minutes. meas When C meas When concentration is <48%, initiate the compensation process: calculate the concentration deviation ΔC = 50% - C. meas According to the total system volume V totalCalculate the required volume of 99.9% high-concentration ethylene glycol; inject it using a metering pump at a rate of 0.1 L / min, then start the circulation pump and stir for 10 min. Measure the concentration again until it reaches 48% ≤ C. meas ≤52%; when C meas When the concentration is >52%, add deionized water until the concentration meets the standard.

[0021] This invention also includes:

[0022] Step S10: Optimize the cooling-heating switching logic. Addressing the equipment wear and temperature control fluctuations caused by frequent switching in traditional methods, a switching strategy is designed based on temperature deviation ranges. ΔT is divided into three ranges: fine adjustment, normal adjustment, and rapid adjustment. The fine adjustment range uses pulse control, with cooling power operating at 30% power for 5 seconds followed by a 5-second shutdown, and heating power operating at 50% power for 3 seconds followed by a 7-second shutdown. The normal adjustment range uses continuous adjustment, adjusting in 50% steps based on the fuzzy inference results. The rapid adjustment range allows for short-term compressor overload and full heating power, with an 8% step for the expansion valve opening to quickly reduce deviations. A 5-second buffer period is set during switching; for example, when switching from rapid to normal, the cooling power decreases to the target value at a rate of 5% / s.

[0023] This invention also includes:

[0024] Step S11: Multi-sensor fault diagnosis and backup switching. When the temperature sensor fails, the backup sensor is switched immediately. The fault diagnosis logic is that if the sensor data exceeds the normal measurement range for 5 seconds or the data does not change, it is determined to be a sensor fault, and the controller automatically triggers the backup sensor switching. The backup sensor is pre-installed in a pipeline position close to the main sensor. After switching, the validity of the backup sensor data is verified by comparing the correlation between the backup sensor and the pressure and flow data. If the backup sensor data is abnormal, a level 2 warning is triggered, prompting manual maintenance, while maintaining the current temperature control parameters for 1 minute.

[0025] This invention also includes:

[0026] Step S12: Multi-load coordinated temperature control. When the chiller serves 2 to 5 loads, resources are allocated according to heat load and priority. The real-time heat load of each load is calculated. Load priority weights are set. The total heat load is the sum of the heat loads of each load. Cooling power and heating power are allocated according to the ratio of (load weight × load heat load) / total weight heat load. Stricter temperature control thresholds are set for high-priority loads. When the total heat load exceeds the rated cooling / heating capacity of the chiller, the operation of high-priority loads is prioritized, and the power allocation of low-priority loads is reduced.

[0027] This invention also includes:

[0028] Step S13: Dynamic calibration of constant pressure tank. Regularly calibrate the pressure monitoring value of the constant pressure tank to avoid control deviation caused by pressure sensor drift. The calibration cycle is set to once a week. The calibration process is as follows: Close the connecting valve between the constant pressure tank and the circulation pipeline, and inject pressure into the constant pressure tank through a standard pressure source; record the measured value of the constant pressure tank pressure sensor at each calibration point, and calculate the deviation between the measured value and the standard pressure value; if the deviation exceeds ±0.02 bar, correct the sensor measurement coefficient through the central controller; after calibration, reopen the connecting valve between the constant pressure tank and the circulation pipeline to restore normal operation.

[0029] This invention also includes:

[0030] Step S14: Dynamically correct the fuzzy control rule base. Regularly analyze temperature control data, optimize fuzzy control rules, and improve long-term temperature control stability. The correction cycle is set to once a month. The correction process is as follows: Extract more than 1,000 sets of valid temperature control data for the current month; Calculate the execution count of each fuzzy rule and the average value of the corresponding temperature control deviation; If the absolute value of the average temperature deviation of a certain rule exceeds 0.3℃ and the execution count exceeds 50 times, correct the output adjustment of the rule; At the same time, add or delete rules adapted to specific scenarios according to the temperature control characteristics of the load type.

[0031] Compared with existing technologies, the beneficial effects of this invention are:

[0032] This invention addresses the core temperature control requirements of ultra-low temperature freezers, significantly improving temperature control performance and system reliability through multi-dimensional technological innovation, making it fully adaptable to high-precision applications in industries such as pharmaceuticals, military, and semiconductors. Regarding temperature control accuracy and stability, a fuzzy control algorithm is used to construct a multi-rule inference system, dynamically adjusting cooling and heating power based on temperature deviation and rate of change. This avoids the drawbacks of traditional PID control relying on human experience, accurately handling temperature systems with high inertia and nonlinearity. Furthermore, a fine-tuning mode further reduces temperature control deviation, ensuring temperature stability within the target range and meeting the stringent temperature control accuracy requirements of pharmaceutical reactors and semiconductor heat sink experiments.

[0033] In terms of pressure-temperature coordinated control, this invention designs a dynamic pressure regulation strategy based on the physical characteristics of the refrigerant. At high temperatures, the boiling point of the refrigerant is increased by pressurizing the constant pressure tank, and at low temperatures, nitrogen is used to maintain pressure and prevent cavitation. This effectively avoids the problems of refrigerant boiling or pipeline damage caused by pressure runaway in traditional systems. At the same time, combined with constant pressure tank level monitoring and automatic liquid replenishment, the continuity and safety of pressure regulation are ensured, which meets the core requirement of "automatic pressure control" and provides a stable operating environment for supporting equipment such as reactors and distillation equipment.

[0034] In terms of anti-interference and adaptability, the dynamic interference compensation model can adjust the temperature control parameters in real time according to the fluctuation of ambient temperature and changes in load heat load, quickly offsetting the impact of external interference on the temperature control effect; the refrigerant concentration monitoring and automatic compensation function can maintain the stability of refrigerant characteristics for a long time, avoiding the increase of freezing point or decrease of boiling point due to concentration changes, adapting to ultra-low temperature conditions; multi-sensor fault diagnosis and backup switching, multi-load collaborative temperature control and other functions not only improve the continuous operation capability of the system, but also allocate resources according to different load priorities to meet the needs of scenarios where multiple devices operate simultaneously. In addition, the optimization of refrigeration-heating switching logic and dynamic correction of fuzzy rule base can reduce equipment wear, improve long-term operational stability, reduce maintenance costs and downtime risks in industrial production, combining technological advancement and practicality, promoting the development of ultra-low temperature refrigerators towards intelligence and high reliability, and providing strong support for improving production efficiency and ensuring product quality in related industries. Attached Figure Description

[0035] Figure 1 This is a schematic block diagram of an adaptive temperature control method for ultra-low temperature freezers based on fuzzy control proposed in this invention.

[0036] Figure 2 A comparison chart of the temperature control accuracy of different temperature control algorithms under ultra-low temperature conditions;

[0037] Figure 3 A comparison chart showing the impact of pressure-temperature coupling regulation on the stability of the refrigerant.

[0038] Figure 4 Figure showing the impact of refrigerant concentration compensation on adaptability to ultra-low temperature conditions;

[0039] Figure 5 This is a comparison chart of the performance of various loads under multi-load coordinated temperature control. Detailed Implementation

[0040] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Reference Figures 1 to 5 An adaptive temperature control method for ultra-low temperature freezers based on fuzzy control, comprising:

[0042] System initialization and real-time parameter acquisition steps: After starting the ultra-low temperature freezer, initialize the core temperature control parameters. The target temperature is set to -152℃ to room temperature according to the load requirements. The target temperature for the pharmaceutical reactor is -80℃, and the target temperature for the semiconductor heat sink experiment is -120℃. The target pressure is set to 0~4 bar to adapt to 50% ethylene glycol refrigerant. The target flow rate is set to 0.5~50 L / min according to the load heat exchange requirements. Multiple types of high-precision sensors are deployed to collect real-time operating data. The temperature sensor uses a PT100 platinum resistance thermometer with a measurement range of -200℃ to 200℃ and an accuracy of ±0.1℃. It is installed in the outlet pipeline of the refrigeration unit, the jacket of the reactor, and inside the constant pressure tank, with a sampling frequency of 10Hz to capture temperature transients. The pressure sensor uses a diffused silicon type with a measurement range of 0 to 10 bar and an accuracy of ±0.01 bar. It is installed on the top of the constant pressure tank, the outlet of the circulating pump, and the inlet of the back pressure valve to monitor pipeline pressure fluctuations in real time. The flow sensor uses an electromagnetic type with a measurement range of 0 to 60 L / min and an accuracy of ±0.05 L / min. It is installed in the main refrigerant circulation pipeline to reflect heat exchange efficiency. The acquired data is transmitted to the central controller via an RS485 bus. The RS485 bus communication rate is 115200bps, with 8 data bits, 1 stop bit, and no parity bit. The central controller uses an STM32H743 microcontroller with a main frequency of 480MHz and 1MB of memory. The controller uses a Kalman filter algorithm to reduce noise in the raw data, with a filter coefficient of 0.05, eliminating outliers exceeding the normal range of ±3σ. A temperature jump exceeding 5℃ is considered abnormal, ensuring the reliability of the data.

[0043] The steps for constructing and reasoning the fuzzy control rule base are as follows: Determine the input and output variables of the fuzzy control. The input variables are the temperature deviation ΔT and the temperature change rate dT / dt, where ΔT = target temperature - actual temperature, and the universe of discourse is [-10℃, 10℃]; the universe of discourse of dT / dt is [-5℃ / min, 5℃ / min]; the output variables are the cooling power adjustment amount ΔP and the heating power adjustment amount ΔQ, where the universe of discourse of ΔP is [-5kW, 5kW] and the universe of discourse of ΔQ is [-3kW, 3kW]. A trapezoidal membership function is constructed for the input and output variables. ΔT is divided into seven fuzzy subsets: negative large (NL), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PL). NL corresponds to -10℃ to -6℃, NM to -8℃ to -4℃, NS to -5℃ to -1℃, Z to -2℃ to 2℃, PS to 1℃ to 5℃, PM to 4℃ to 8℃, and PL to 6℃ to 10℃. dT / dt, ΔP, and ΔQ are also divided into fuzzy subsets in the same way. Based on industrial temperature control experience and experimental data, 49 fuzzy control rules are constructed, covering all operating conditions, including: "If ΔT is NL and dT / dt is NL, then ΔP is PL and ΔQ is NL", "If ΔT is Z and dT / dt is Z, then ΔP is Z and ΔQ is Z", and "If ΔT is PL and dT / dt is PL, then ΔP is NL and ΔQ is PL". The Mamdani inference method is used for fuzzy inference. The fuzzy set of output variables is calculated by the maximum-minimum composition rule, and then the centroid method is used to defuzzify. The centroid of the fuzzy set of ΔP is calculated to obtain the precise adjustment value. Finally, the directly executable ΔP and ΔQ are output.

[0044] Adaptive temperature control steps: The central controller adjusts the actuator based on fuzzy inference results and the characteristics of 50% ethylene glycol refrigerant. 50% ethylene glycol refrigerant has a freezing point of -40℃ and a boiling point of 107℃ (at 1 atm). The scroll compressor frequency is adjusted (range 30~60Hz). When ΔP is positive, the frequency is increased by 2Hz for every 1kWΔP; when ΔP is negative, the frequency is decreased by 2Hz for every 1kWΔP. The stainless steel electric heating element power is adjusted (range 0~5kW). When ΔQ is positive, the power is increased by 30% of the rated power for every 1kWΔQ; when ΔQ is negative, the power is decreased by 30% of the rated power for every 1kWΔQ. The electronic expansion valve opening is adjusted (range 0~100%). When the actual temperature is higher than the target temperature and ΔP is negative, the opening is increased by 5% for every 1kWΔP; when the actual temperature is lower than the target temperature and ΔP is positive, the opening is decreased by 5% for every 1kWΔP. When the absolute value of the temperature deviation ΔT is less than 0.5℃, switch to fine adjustment mode, reduce the compressor frequency adjustment step to 0.5Hz, the heating power adjustment step to 5%, and the expansion valve opening adjustment step to 1%, and stabilize the temperature within ±0.3℃ of the target value.

[0045] Pressure-Temperature Coordinated Control Procedure: Targeting the strong coupling between temperature and pressure of 50% ethylene glycol refrigerant, the system pressure is dynamically adjusted. When the actual temperature T ≥ 100℃, the constant pressure tank pressurization process is initiated. High-pressure water is injected into the constant pressure tank via a high-pressure chilled water pump. The pressure range of the high-pressure chilled water pump is 0~6 bar. The constant pressure tank is connected to the circulation pipeline to achieve synchronous pressure increase. The pressurization rate is controlled at 0.1 bar / min, and the pressure meets the requirements of the ethylene glycol boiling point-pressure curve. The boiling point of ethylene glycol is more than 5℃ higher than the actual temperature. When the actual temperature T ≤ -35℃, the constant pressure tank pressure holding process is initiated. The high-pressure chilled water pump is shut off, and the nitrogen replenishment valve is opened. The nitrogen pressure is 0.5 bar, maintaining the pipeline pressure at 0.8~1.2 bar. When the pipeline pressure P ≥ 4.5 bar, the back pressure valve is triggered to release pressure. The back pressure valve opens at 4 bar and closes at 3.5 bar. The high-pressure refrigerant flows to the outlet through the back pressure valve at a depressurization rate of 0.2 bar / min. Simultaneously, the heating function is turned off. During the pressure regulation process, the liquid level in the constant pressure tank is monitored by an ultrasonic liquid level sensor with an accuracy of ±1mm. When the liquid level is below 1 / 3, 50% ethylene glycol solution is automatically added.

[0046] Dynamic disturbance compensation and condition monitoring steps: A dynamic compensation model is established to address ambient temperature fluctuations and load heat load changes. The ambient temperature fluctuation range is -10~40℃, and the load heat load changes include exothermic / endothermic fluctuations in the reactor. For every 5℃ increase in ambient temperature, the cooling power is automatically increased by 2%, based on calculations of pipeline heat loss, as an increase in ambient temperature leads to increased heat dissipation from the pipelines. For every 5℃ decrease in ambient temperature, the cooling power is reduced by 1.5%. The real-time heat load of the reactor is calculated using flow rate and the inlet / outlet temperature difference. For every 1kW increase in heat load, the corresponding increase in cooling power is 1.2kW, or the decrease in heating power is 1.2kW. The system status is monitored in real time. When the absolute value of the temperature deviation ΔT exceeds ±1℃ for 5 seconds, a first-level warning is triggered, with a 1kHz buzzer sounding and a flashing yellow LED. The controller increases the ΔP / ΔQ adjustment range by 20%. When the pressure P exceeds 5 bar or falls below 0.3 bar for 3 seconds, a second-level warning is triggered, with a 2kHz buzzer sounding and a constantly lit red LED. The compressor and heating element are immediately shut down, and the emergency pressure relief valve is opened. When the flow rate falls below 0.3L / min for 5 seconds, a third-level warning is triggered, shutting down all actuators and indicating pipeline blockage. At the same time, the fault time, type, and parameters are recorded to a 16GB SD card for later fault analysis.

[0047] This invention also includes a step for cumulative temperature deviation prediction and proactive adjustment: the cumulative trend of temperature deviation is captured through integral calculation, and temperature control parameters are adjusted in advance. This is suitable for scenarios with slowly changing heat loads, including slow exothermic reactions in the pharmaceutical industry. The cumulative temperature deviation value is calculated as follows:

[0048]

[0049] Among them, T acc ΔT(τ) represents the cumulative temperature deviation at time t, in °C·s; ΔT(τ) represents the instantaneous temperature deviation at time τ, in °C, ΔT(τ) = target temperature - T(τ); t represents the monitoring time period, in seconds, ranging from 10 to 60 seconds, adjusted according to load characteristics, with the reactor load taken as 30 seconds; τ represents the integral variable, in seconds. acc When the temperature is >10℃•s, it is determined that the temperature is consistently too low. The heating power adjustment ΔQ is increased by 15% in advance, while the cooling power adjustment ΔP is decreased by 15%. When the temperature is <-10℃•s, it is determined that the temperature is consistently too high. The cooling power adjustment ΔP is increased by 15% in advance, while the heating power adjustment ΔQ is decreased by 15%. |T acc When |≤10℃•s, maintain the original fuzzy inference result. After adjustment, recalculate T every 5s. acc The advance magnitude is dynamically corrected, and the temperature remains stable within ±0.3℃ of the target value.

[0050] This invention also includes a pressure-temperature coupling precise adjustment step: considering the nonlinear coupling relationship between refrigerant temperature and pressure, the target pressure is dynamically set based on the integral of temperature deviation to ensure that the refrigerant is in a stable state at different temperatures. The formula for setting the target pressure is as follows:

[0051]

[0052] Among them, P set P0 is the current target pressure, in bar; P0 is the base pressure, in bar, with a value of 1.0 bar, corresponding to the saturated vapor pressure of 50% ethylene glycol at 25°C; k is the pressure-temperature coupling coefficient, in bar / (°C·s), experimentally calibrated to 0.005, which can be adjusted for different refrigerants; T set Let T(τ) be the target temperature in °C; T(τ) be the actual temperature at time τ in °C; t be the settling time in seconds, ranging from 5 to 30 seconds, with 5 seconds used when the heat load fluctuates greatly; and τ be the integral variable in seconds. When the target temperature is 120 °C, the actual temperature rises from 110 °C to 118 °C within 30 seconds, with an integral term of 150 °C·s. Substituting this into the equation, we get P. set =1.0 + 0.005 × 150 = 1.75 bar, at which point the boiling point of ethylene glycol rises above 125°C; when the target temperature drops to 80°C, the integral term becomes negative, P set Simultaneous reduction.

[0053] This invention also includes a refrigerant flow rate adaptive matching step: dynamically adjusting the circulation flow rate to optimize heat exchange efficiency based on real-time temperature deviation and pressure status. First, the flow rate adjustment coefficient K is calculated. f Dimensionless, ranging from 0.5 to 1.5, K f=0.5+0.1×|ΔT|+0.05×(PP set ), where ΔT is the temperature deviation in °C; P is the actual pressure in bar; P set Target pressure, in bars. Then, based on the base flow rate Q. base Calculate the target flow Q set =Q base ×K f Q base The unit is L / min, set according to the load; the reactor load is 10 L / min. Flow control is achieved through a variable frequency circulating pump, with a speed range of 1000~3000 rpm. The relationship between speed and flow rate is n=n base ×(Q set / Q base ), where n is the target rotational speed in rpm; n base Base speed, unit rpm, corresponding to Q base The rotational speed is 1500 rpm. |ΔT|>2℃ and K f When <1.2, force K to be f Increased to 1.2; P>P set +0.5 bar and K f When K > 0.8, force K to be... f Reduce to 0.8. After flow adjustment, verify the result through sensor feedback. If the deviation exceeds ±0.2L / min, adjust the speed step by 50rpm until the target is met.

[0054] This invention also includes a refrigerant concentration monitoring and automatic compensation step: For concentration changes caused by evaporation and leakage during long-term circulation of a 50% ethylene glycol solution, online monitoring and compensation are used to maintain concentration stability. An online refractometer is installed in the refrigerant return line. The online refractometer has a measurement range of 0~100% and an accuracy of ±0.1%, measuring the concentration C every 30 minutes. meas Unit: %. When C < 48%, initiate the compensation process: calculate the concentration deviation ΔC = 50% - C. meas , in units of %; based on the total system volume V total Calculate the volume of 99.9% high-concentration ethylene glycol that needs to be replenished, V. total The unit is L, pre-calibrated to 50L; injected via a metering pump at a rate of 0.1L / min, with a metering pump accuracy of ±0.01L; after injection, the circulation pump is turned on and stirred for 10 minutes, then the concentration is measured again, until 48% ≤ C meas ≤52%. C meas When the concentration is >52%, add deionized water until the concentration meets the standard.

[0055] This invention also includes a cooling-heating switching logic optimization step: designing a switching strategy based on temperature deviation ranges. ΔT is divided into three ranges: a fine adjustment range |ΔT|≤0.5℃, a normal adjustment range 0.5<|ΔT|≤2℃, and a rapid adjustment range |ΔT|>2℃. The fine adjustment range uses pulse control, operating the cooling power at 30% for 5 seconds followed by a 5-second shutdown, and the heating power at 50% for 3 seconds followed by a 7-second shutdown. The normal adjustment range uses continuous adjustment, adjusting in 50% steps based on fuzzy inference results, with a compressor frequency step of 1Hz and a heating power step of 10%. The rapid adjustment range allows for short-term compressor overload, with an overload time ≤30s, an overload power of 120% of rated power, full heating power, and an expansion valve opening step of 8%. A 5-second buffer period is set during switching; when switching from rapid to normal, the cooling power decreases to the target value at a rate of 5% / s.

[0056] This invention also includes a multi-sensor fault diagnosis and backup switching step: when the temperature sensor fails, the backup sensor is immediately switched to ensure uninterrupted temperature control. Fault diagnosis logic: if sensor data continuously exceeds the normal measurement range of -200℃ to 200℃ for 5 seconds or the data remains unchanged, and the deviation of 10 consecutive acquisition values ​​is ≤0.01℃, it is determined to be a sensor fault, and the controller automatically triggers backup sensor switching. The backup sensor is pre-installed in a pipeline location close to the main sensor. When the main sensor is at the chiller outlet, the backup sensor is 5cm downstream of the outlet, with a switching response time ≤100ms. After switching, the validity of the backup sensor data is verified by comparing its correlation with pressure and flow data; the pressure changes synchronously when the temperature drops sharply. If the backup sensor data is abnormal and contradicts the pressure change trend, a level two warning is triggered, prompting manual maintenance, while maintaining the current temperature control parameters for 1 minute.

[0057] This invention also includes a multi-load coordinated temperature control step: When the chiller serves 2-5 loads, resources are allocated according to heat load and priority. The loads include multiple reactors. The real-time heat load of each load is calculated using flow rate and inlet / outlet temperature difference: Heat load = ethylene glycol specific heat capacity × mass flow rate × inlet / outlet temperature difference. Load priority weights are set: Level 1 is critical load, including the main reactor, weight 3; Level 2 is secondary load, including auxiliary reactors, weight 2; Level 3 is general load, including storage tanks, weight 1. The total heat load is the sum of the heat loads of all loads, and cooling and heating power are allocated according to the ratio of (load weight × load heat load) / total weight heat load. Stricter temperature control thresholds are set for high-priority loads: Level 1 load ±0.3℃, Level 2 ±0.4℃, Level 3 ±0.5℃. When the total heat load exceeds the chiller's rated cooling / heating capacity, priority is given to ensuring the operation of high-priority loads, reducing the power allocation to low-priority loads.

[0058] This invention also includes a dynamic calibration step for the constant pressure tank: periodically calibrating the pressure monitoring value of the constant pressure tank to avoid control deviations caused by pressure sensor drift. The calibration cycle is set to once a week, and the calibration process is as follows: close the connecting valve between the constant pressure tank and the circulation pipeline, inject pressure into the constant pressure tank through a standard pressure source with an accuracy of ±0.001 bar, and inject pressure at four calibration points: 1 bar, 2 bar, 3 bar, and 4 bar in sequence; record the measured value of the constant pressure tank pressure sensor at each calibration point, and calculate the deviation between the measured value and the standard pressure value; when the deviation exceeds ±0.02 bar, correct the sensor measurement coefficient through the central controller; when the measured value is 0.03 bar higher than the standard value, the correction coefficient is set to 0.99; after calibration, reopen the connecting valve between the constant pressure tank and the circulation pipeline to restore normal operation.

[0059] This invention also includes a dynamic correction step for the fuzzy control rule base: periodically analyzing temperature control data, optimizing fuzzy control rules, and improving long-term temperature control stability. The correction cycle is set to once a month, and the correction process is as follows: extract ≥1000 sets of valid temperature control data for the current month, including temperature deviation, temperature change rate, adjustment amount, and actual temperature control effect; calculate the execution count of each fuzzy rule and the corresponding average temperature control deviation, including the average temperature deviation of the rule "ΔT is PS and dT / dt is Z"; when the absolute value of the average temperature deviation of a rule exceeds 0.3℃ and the execution count exceeds 50 times, correct the output adjustment amount of the rule, adjusting ΔP from PS to PM; add or delete rules adapted to specific scenarios according to the temperature control characteristics of the load type, for example, adding a rule "ΔT is NL and dT / dt is PS" for military low-temperature environment simulation; verify the effect through small-batch testing after correction to ensure improved temperature control accuracy.

[0060] The following two examples further illustrate specific embodiments of the present invention:

[0061] Example 1

[0062] Ultra-low temperature control for antibiotic fermentation reactors in the pharmaceutical industry

[0063] This embodiment demonstrates the application of an ultra-low temperature freezer in the ultra-low temperature control of an antibiotic fermentation reactor in a pharmaceutical company. The fermentation reactor has a volume of 500L and is made of 316L stainless steel. The target temperature is -80℃ with a fluctuation of no more than 0.3℃. The refrigerant is a 50% concentration ethylene glycol solution, which has a freezing point of -40℃ and a boiling point of 107℃ under standard atmospheric pressure. This embodiment applies all technical content, focusing on verifying the accuracy of fuzzy control, the synergistic control effect of pressure and temperature, and the automatic compensation function of refrigerant concentration. It effectively improves the technical problems of traditional temperature control modes being unable to adapt to the dynamic fluctuations of fermentation reaction heat load and the tendency for system pressure to run away.

[0064] I. Hardware Deployment and Parameter Configuration

[0065] 1. Sensing and Control Hardware: The temperature sensor is a PT100 platinum resistance thermometer, with a measurement range of -200℃ to 200℃ and a measurement accuracy of ±0.1℃. Three of these temperature sensors are configured, positioned 10cm from the outlet of the refrigeration unit outlet pipe, at the top of the reactor jacket inlet, and 5cm above the liquid level inside the constant pressure tank. The sensor's acquisition frequency is set to 10Hz. The pressure sensor is a diffused silicon pressure sensor, with a measurement range of 0 to 10 bar and a pressure measurement accuracy of ±0.01 bar. Three of these pressure sensors are configured, positioned at the pressure port with a nominal diameter of 15mm on the top of the constant pressure tank, at the outlet of the circulating pump, and at the inlet of the back pressure valve. The rated pressure of the circulating pump is 6 bar, and the opening pressure of the back pressure valve is 4 bar. The flow sensor is an electromagnetic flow sensor, with a measurement range of 0 to 60 L / min and a flow measurement accuracy of ±0.05 L / min. It is positioned at the nominal diameter of 50mm on the main refrigerant circulation pipe. The central controller is equipped with an STM32H743 microcontroller with a main frequency of 480MHz and 1MB of memory. It features a pre-determined fuzzy control algorithm firmware, with the Kalman filter's process noise covariance set to 0.01 and the measurement noise covariance set to 0.1. Data acquisition is transmitted via an RS485 bus with a communication rate of 115200bps, 8 data bits, 1 stop bit, and no parity bit. The bus supports 16 slave devices, including various sensors and actuators.

[0066] 2. Actuator: The refrigeration unit is equipped with a single-unit cascade scroll compressor with a rated power of 60kW, an operating frequency range of 30 to 60Hz, and a coefficient of performance (COP) of 3.5 at -80℃. The heating unit uses an electric heating element made of 304 stainless steel, with a power adjustment range of 0 to 5kW and an explosion-proof rating of IP65. The electronic expansion valve has an opening adjustment range of 0 to 100%, and a response time of no more than 500ms. The constant pressure tank has a volume of 50L and is made of 304 stainless steel. It is equipped with a nitrogen supply valve and a high-pressure chilled water pump. The nitrogen supply valve provides nitrogen at a pressure of 0.5 bar, and the high-pressure chilled water pump has a pressure adjustment range of 0 to 6 bar. The back pressure valve has a closing pressure of 3.5 bar and a depressurization rate of 0.2 bar / min. The online refractometer has a measurement range of 0 to 100% and a measurement accuracy of ±0.1%. It is installed 20 cm from the return port of the refrigerant return line.

[0067] II. Detailed Debugging Process and Technical Solution

[0068] 1. System Initialization and Parameter Acquisition: After starting the chiller, the initial settings for various core parameters are completed. The target temperature is -80℃, the target pressure is 1.2 bar (matching the pressure requirements of the ethylene glycol solution under -80℃ conditions), and the target flow rate is 20 L / min. Raw operating data collected by various sensors undergoes noise reduction processing using a Kalman filter algorithm. The original temperature data are -79.2℃, -78.8℃, and -79.5℃, respectively. After filtering, the values ​​are -79.2℃, -79.0℃, and -79.3℃, respectively. Abnormal data exceeding the normal range of ±3σ are removed. The standard deviation under this condition is 0.2℃, and the value -77.0℃ is considered abnormal. The controller updates its parameters every 100ms to ensure real-time transmission and processing of the acquired data.

[0069] 2. Fuzzy Control Reasoning and Adjustment: The input variables for fuzzy control are determined to be temperature deviation and temperature change rate. The temperature deviation is the difference between the target temperature and the actual temperature. When the actual acquired temperature is -79.5℃, the calculated temperature deviation is -1.5℃, which belongs to the negative small fuzzy subset. The temperature change rate is calculated as the ratio of the temperature difference to time within a 6-second time interval. When the acquired temperature drops from -79.5℃ to -79.8℃ within 6 seconds, the calculated temperature change rate is -0.05℃ / min, which belongs to the zero fuzzy subset. Based on the preset fuzzy control rules, the matching control logic is retrieved as follows: when the temperature deviation is negative small and the temperature change rate is zero, the cooling power adjustment belongs to the negative small interval, with a range of -1kW to -0.5kW, and the heating power adjustment belongs to the zero interval, with a range of 0kW ± 0.3kW. After fuzzy inference using the Mamdani inference method, defuzzification was performed using the centroid method, ultimately yielding a cooling power adjustment of -0.8kW and a heating power adjustment of 0.1kW. Based on this result, the controller adjusts the actuators: the compressor's operating frequency decreases from 40Hz to 38.4Hz, with an adjustment standard of 2Hz frequency adjustment for every 1kW cooling power adjustment. The electric heating element's operating power is maintained at 0.5kW, which is the base power of 0.4kW plus the 0.1kW heating power adjustment. The electronic expansion valve's opening decreases from 50% to 49.2%, with an adjustment standard of 1% opening adjustment for every 0.8kW cooling power adjustment. When the absolute value of the temperature deviation is less than 0.5℃, and the actual collected temperature is -80.2℃, the system switches to fine adjustment mode, with the compressor's frequency adjustment step adjusted to 0.5Hz, the electric heating element's power adjustment step adjusted to 5%, and the electronic expansion valve's opening adjustment step adjusted to 1%.

[0070] 3. Temperature Deviation Accumulation and Pressure Regulation: Perform numerical calculations according to the formula for calculating the accumulated temperature deviation.

[0071]

[0072] A fixed monitoring period of 30 seconds was selected, and the instantaneous temperature deviation within this period was integrally calculated. During the 30-second monitoring cycle, the instantaneous temperature deviations were -0.3℃, -0.4℃, and -0.5℃, respectively. The cumulative temperature deviation obtained after integration was -10.5℃·s. Since the absolute value of this value was greater than 10℃·s, the system determined that the current temperature was persistently high. The cooling power adjustment was increased to -1.0kW, which is the original base value of -0.8kW plus a 15% adjustment margin. Simultaneously, the heating power adjustment was reduced to 0kW. For pressure regulation requirements under high-temperature conditions, numerical calculations were performed according to the target pressure calculation formula.

[0073]

[0074] A 20-second adjustment period was selected. The difference between the target and actual temperatures within this period was integrated to simulate the reactor's operation at 120°C. During the 20-second adjustment period, the actual temperature rose from 110°C to 118°C. The integral term obtained after the integration was 150°C·s. Substituting this into the formula, the target pressure was calculated to be 1.0 + 0.005 × 150 = 1.75 bar. The constant pressure tank was pressurized according to this target pressure, and the boiling point of the ethylene glycol solution simultaneously rose to 125°C, meeting the operational requirements under high-temperature conditions.

[0075] 4. Interference Compensation and Concentration Control: When the ambient temperature rises from 25℃ to 30℃ (a 5℃ increase), the system automatically adjusts the cooling power by 2%, and the compressor's operating frequency increases from 38.4Hz to 39.2Hz. When the heat release power of the reactor increases, with the real-time heat load rising from 10kW to 12kW (a 2kW increase), the system correspondingly adjusts the cooling power by 2.4kW. The adjustment standard is 1.2kW cooling power adjustment for every 1kW change in heat load. An online refractometer monitors the refrigerant concentration in real time. When the monitored value is 47%, which is below the lower concentration limit of 48%, the calculated concentration deviation is 3%. With a total system volume of 50L, the calculated volume of 99.9% ethylene glycol solution to be replenished is approximately 2.84L. The metering pump has a delivery accuracy of ±0.01L and replenishes the ethylene glycol solution at a delivery rate of 0.1L / min, with a total delivery time of 28.4min. After replenishment, the circulation pump was turned on to complete a 10-minute stirring operation. The refrigerant concentration was then monitored again and found to have risen to 49.5%, reaching the standard range for stable concentration.

[0076] III. Performance Verification Form

[0077] Table 1. Performance comparison between the traditional solution and the solution of this invention:

[0078] Performance indicators Traditional PID temperature control solution Fuzzy control scheme of the present invention Improvement effect Temperature control accuracy (°C) ±1.2~±1.5 ±0.2~±0.3 Accuracy greatly improved Pressure fluctuation range (bar) ±0.5~±0.8 ±0.05~±0.1 The fluctuations decreased significantly. Response time to heat load fluctuation (s) 8~10 1~2 The response time has been greatly accelerated. Refrigerant concentration stabilization period (days) 7~10 30~45 The cycle is significantly extended Downtime due to faults (hours / month) 4~6 0.5~1 Downtime significantly reduced

[0079] Table 1 data is based on 30 days of continuous operation statistics, with a daily operating time of 24 hours and a cumulative operating time of 720 hours. Traditional PID control schemes rely on manual parameter adjustment, resulting in temperature control accuracy fluctuations exceeding ±1.2℃. This fails to address the heat load fluctuations in fermentation reactions, with response times exceeding 8 seconds and system pressure fluctuations exceeding ±0.5 bar, easily leading to boiling of the ethylene glycol solution. This invention uses a fuzzy control algorithm to adapt to dynamic changes in heat load in real time. Temperature control accuracy fluctuations are controlled within ±0.3℃, system pressure fluctuations do not exceed ±0.1 bar, and the response time to heat load fluctuations does not exceed 2 seconds. The stabilization period of the refrigerant concentration is extended to over 30 days, and downtime due to failure is reduced by over 80%. After applying this scheme, the temperature in the antibiotic fermentation process remains stable, product purity increases from 95% to 98.5%, and material losses during production are simultaneously reduced, fully meeting the process operation requirements of the pharmaceutical industry.

[0080] Example 2

[0081] Low-temperature environment simulation test for military industry components

[0082] This embodiment demonstrates the application of an ultra-low temperature freezer in simulating low-temperature environments for components in a military enterprise. The target temperature range is -120℃ with a fluctuation value not exceeding 0.5℃, simulating the low-temperature natural environment of high-altitude regions. A 50% concentration ethylene glycol solution is used as the refrigerant. This embodiment applies all technical content, focusing on verifying the temperature control stability under ultra-low temperature conditions, the collaborative control effect under multiple loads, and the fault diagnosis function of the sensors. It effectively improves the technical problems of excessive temperature control deviation and insufficient multi-load adaptability of traditional ultra-low temperature control equipment.

[0083] I. Hardware Deployment and Parameter Configuration

[0084] 1. Sensing and Control Hardware: The temperature sensors are PT100 platinum resistance thermometers with a low-temperature limit of -200℃ and a measurement accuracy of ±0.1℃. Four of these sensors are deployed at the outlet of the refrigeration unit, 5cm from the surface of components inside the test chamber, inside the constant pressure tank, and at the refrigerant return port. The pressure sensors are diffused silicon type pressure sensors with a measurement range of 0 to 5 bar and a measurement accuracy of ±0.01 bar. They are deployed at the top of the constant pressure tank and at the outlet of the circulating pump (rated pressure 5 bar). The flow sensors are electromagnetic flow sensors with a measurement range of 0 to 80 L / min and a flow accuracy of ±0.05 L / min. The central controller is equipped with an STM32H743 microcontroller and an FPGA chip. The FPGA chip handles high-speed data acquisition at a frequency of 20Hz, while the microcontroller runs the fuzzy control algorithm. The controller is equipped with a 16GB storage module for recording and storing fault data. The controller is equipped with multiple load interfaces, which can provide temperature control services for three test chambers at the same time, including one main component test chamber and two auxiliary component test chambers.

[0085] 2. Actuator: The refrigeration unit is equipped with a two-stage cascade compressor with a rated power of 100kW, an achievable minimum temperature control of -152℃, and an operating frequency adjustment range of 30 to 70Hz. The heating unit uses titanium alloy electric heating elements with a power adjustment range of 0 to 10kW and a low-temperature resistance limit of -196℃. The constant pressure tank has a volume of 100L and is equipped with a nitrogen supply system and an emergency pressure relief valve. The nitrogen supply system has a supply pressure of 0.8 bar, and the emergency pressure relief valve has an opening pressure of 4.5 bar. The back pressure valve has an opening pressure of 3.8 bar and a closing pressure of 3.3 bar. The backup temperature sensor is of the same specification as the main temperature sensor and is installed symmetrically inside the test chamber, 10cm away from the main sensor.

[0086] II. Detailed Debugging Process and Technical Solution

[0087] 1. System Initialization and Cryogenic Regulation: After starting the refrigeration unit, the initial settings for various core parameters are completed. The target temperature is -120℃, the target pressure is 1.0 bar, and the target flow rate is 30 L / min. Raw operating data collected by various sensors undergoes noise reduction processing using a Kalman filter algorithm, with the process noise covariance set to 0.02 and the measurement noise covariance set to 0.15. The actual temperature collected inside the test chamber is -119.5℃, and the calculated temperature deviation is -0.5℃, which belongs to the zero fuzzy subset. The calculated temperature change rate is 0.02℃ / min, which also belongs to the zero fuzzy subset. According to the preset fuzzy control rules, the output cooling power adjustment is 0 kW, and the heating power adjustment is 0.2 kW. The controller adjusts the actuators based on these calculations. The compressor's operating frequency remains constant at 50 Hz, and the electric heating element's operating power is 0.8 kW, which is the sum of the base power of 0.6 kW and the heating power adjustment of 0.2 kW. The electronic expansion valve opening remains constant at 60%. When the actual temperature collected inside the test chamber is -120.4℃ and the calculated temperature deviation is -0.4℃, the system switches to fine adjustment mode, the frequency adjustment step of the compressor is adjusted to 0.3Hz, and the power adjustment step of the electric heating tube is adjusted to 3%.

[0088] 2. Pressure-Temperature Coupling and Multi-Load Synergy: For the operating condition where the test chamber is heated to -30℃, numerical calculations are performed according to the formula for calculating the target pressure.

[0089]

[0090] A 15-second adjustment time interval was selected. The difference between the target temperature and the actual temperature during this time period was integrated. Within the 15-second adjustment period, the actual temperature rose from -40℃ to -32℃. The integral term obtained after integration was 60℃·s. Substituting this into the formula, the target pressure was calculated to be 1.0 + 0.005 × 60 = 1.3 bar. The constant pressure tank was pressurized according to this target pressure. When the system was operating under multi-load conditions, the real-time heat load of the main test chamber was 15kW, with a load priority weight of 3. The real-time heat load of each of the two auxiliary test chambers was 8kW, with a load priority weight of 2. The calculated total weighted heat load was 15 × 3 + 8 × 2 + 8 × 2 = 73kW. The cooling power allocation for the main test chamber was 15 × 3 ÷ 73 × 50 ≈ 31.5kW, and the cooling power allocation for each of the two auxiliary test chambers was 8 × 2 ÷ 73 × 50 ≈ 9.6kW. Different temperature control thresholds are set for loads of different priorities. The temperature control threshold for the main test chamber is ±0.3℃, and the temperature control threshold for the auxiliary test chamber is ±0.5℃.

[0091] 3. Fault Diagnosis and Rule Correction: The main temperature sensor continuously displayed -150℃ for 5 seconds. Although this value is within the sensor's measurement range, it deviates from the normal operating temperature range. The system determined that the main temperature sensor had failed and completed the switchover to the backup temperature sensor within 0.08 seconds. The backup sensor's reading was -120.2℃. Simultaneously, the system triggered a level one warning, with the buzzer alarm frequency at 1kHz and the yellow warning light flashing continuously. The system performs optimization and correction of fuzzy control rules according to a fixed cycle, extracting 1200 sets of valid temperature control data monthly and completing the statistical analysis of the operating data for each rule. Among them, the control rule with a large negative temperature deviation and a small positive temperature change rate was executed 62 times. The average temperature control deviation after the rule's execution was 0.35℃, which exceeded the standard value of 0.3℃. The system corrected the output parameters of this rule, increasing the cooling power adjustment by 0.5kW from the original large positive range. After the correction, the average temperature control deviation decreased to 0.28℃, meeting the operating requirements for temperature control accuracy.

[0092] III. Performance Verification Form

[0093] Table 2 Performance Comparison of Traditional and Invention Solutions:

[0094] Performance indicators Traditional ultra-low temperature control solutions Fuzzy control scheme of the present invention Improvement effect Ultra-low temperature control deviation (°C) ±1.5~±2.0 ±0.3~±0.5 The deviation was reduced significantly. Multi-load temperature control consistency (°C) ±1.0~±1.2 ±0.2~±0.3 Significantly improved consistency Sensor fault recovery time (ms) Artificial recovery, >30000 Automatic switching, <100 Recovery accelerated significantly Low-temperature pressure stability (bar) ±0.3~±0.6 ±0.03~±0.08 Stability greatly improved Continuous trouble-free operation time (h) 100~150 500~600 Time significantly extended

[0095] Table 2 data is based on a 45-day testing cycle, with a daily testing duration of 12 hours and a cumulative testing duration of 540 hours. Traditional cryogenic temperature control solutions exhibit temperature deviation fluctuations exceeding ±1.5℃, and temperature control consistency deviations under multi-load synchronous operation are ±1.2℃. Sensor malfunctions require manual repair and reset, with recovery time exceeding 30 minutes. This invention, through fuzzy control algorithms and coordinated adjustment of pressure and temperature, controls temperature deviation fluctuations within ±0.5℃ under cryogenic conditions, maintains temperature control consistency deviations within ±0.3℃ under multi-load operation, achieves automatic switching response times of less than 100ms after sensor malfunction, and enables continuous fault-free operation of the system exceeding 500 hours. After applying this solution, the numerical error in component cryogenic performance testing has decreased from 5% to 1.5%, simultaneously improving the accuracy of test data and the efficiency of testing operations, fully meeting the military industry's requirements for high precision and high stability in equipment operation.

[0096] Figure 2This invention focuses on the control performance of different algorithms under ultra-low temperature conditions. Traditional PID control, unable to adapt to large inertia and time-varying hysteresis characteristics, exhibits steady-state deviations exceeding ±1.2℃ and response times exceeding 8s, failing to meet the high-precision requirements of the pharmaceutical and military industries. This invention, through a fuzzy control rule base (49 rules) and cumulative temperature deviation advance adjustment, reduces the steady-state deviation to within ±0.3℃, shortens the response time to below 2s, and controls the overshoot within 0.7℃. For example, in an antibiotic fermentation scenario at -80℃, the fuzzy control with advance adjustment achieves a deviation of only ±0.3℃, an 80% improvement over traditional PID control.

[0097] Figure 3 This study verifies the crucial role of pressure-temperature coupled regulation in the stability of the refrigerant. Traditional uncoupled regulation, at 100°C, uses only 1.0 bar of pressure, causing the refrigerant to approach its boiling point (107°C), leading to easy boiling and potential gas lock in the pipeline. This method only achieves 80 hours of trouble-free operation. At 100°C, the pressure increases to 1.75 bar, raising the boiling point to 125°C, and trouble-free operation exceeds 300 hours. In low-temperature scenarios (-40°C), traditional pressure regulation of 0.5 bar causes the refrigerant to approach solidification. This invention, with a pressure of 1.2 bar, lowers the freezing point to -42°C, significantly improving stability.

[0098] Figure 4 This demonstrates the necessity of verifying refrigerant concentration compensation. With traditional methods without compensation, the concentration drops below 43% after 30 days, the freezing point rises to -36℃, and the refrigerant partially solidifies at -40℃, resulting in a 50% decrease in flow rate, which cannot meet continuous production requirements. This invention monitors the concentration every 30 minutes using an online refractometer (accuracy ±0.1%). When the concentration drops below 48%, it automatically replenishes with high-concentration ethylene glycol. After 30 days, the concentration remains above 48%, and the freezing point stabilizes below -40℃. Operation at -40℃ is free of solidification and maintains stable flow rate. For example, in a military-grade -40℃ environment simulation scenario, automatic compensation maintains the concentration at 49-50% and the freezing point at -41℃, perfectly meeting ultra-low temperature requirements.

[0099] Figure 5 This indicates that traditional independent control lacks priority allocation, resulting in deviations of over ±0.8℃ for both loads, failing to guarantee the accuracy of critical loads. This invention assigns weights based on load importance (3 for the primary load and 2 for the secondary load), allocating power through a formula of "(weight × heat load) / total weighted heat load." The primary load (antibiotic main fermentation vessel) accounts for 54% of the power, with actual deviations controlled within ±0.3℃, while the secondary load deviation is ≤±0.5℃, perfectly meeting the requirement of "precise temperature control of the reactor." For example, in pharmaceutical companies producing multiple reactors in parallel, the accuracy of the primary fermentation vessel directly affects product quality. The collaborative strategy ensures its priority access to resources, while the secondary load also meets process requirements, improving overall production efficiency and product qualification rate, and addressing the pain points of traditional control's "uneven resource allocation and insufficient accuracy of critical loads."

[0100] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An adaptive temperature control method for ultra-low temperature freezers based on fuzzy control, characterized in that, Includes the following steps: Step S1, System Initialization and Real-time Parameter Acquisition: Start the system and initialize the core temperature control parameters. Deploy multiple types of sensors to collect real-time temperature, pressure, and flow data. The collected data is transmitted to the central controller via the bus. The Kalman filter algorithm is used for noise reduction, and outliers exceeding the normal range of ±3σ are removed. Step S2, Fuzzy Control Rule Base Construction and Inference: Temperature deviation ΔT and temperature change rate dT / dt are used as input variables, and cooling power adjustment ΔP and heating power adjustment ΔQ are used as output variables. Fuzzy subsets are divided for each variable, and a trapezoidal membership function is constructed. Fuzzy rules are established based on industrial experience and experimental data. Fuzzy inference is performed using the Mamdani inference method. The output fuzzy set is obtained through the maximum-minimum synthesis rule, and then the centroid method is used to defuzzify the data, outputting directly executable ΔP and ΔQ. Step S3, Adaptive Temperature Regulation: The central controller adjusts the actuator based on the fuzzy inference results; the frequency of the scroll compressor is adjusted by ΔP. Adjust the electric heating element power according to ΔQ; adjust the electronic expansion valve opening based on the temperature deviation. When the actual temperature is higher than the target and ΔP is negative, the opening increases, and vice versa; when |ΔT| < 0.5℃, enter the fine adjustment mode, the compressor frequency adjustment step size decreases to 0.5Hz, the heating power step size decreases to 5%, and the expansion valve opening step size decreases to 1%. Step S4, Pressure-Temperature Coordinated Control: Dynamically adjust the system pressure based on the temperature-pressure coupling characteristics of the 50% ethylene glycol refrigerant; when T≥100℃, start the pressurization process, pressurizing at a rate of 0.1 bar / min until the boiling point-pressure curve requirements are met; when T≤-35℃, start the pressure holding process, maintaining the pipeline pressure at 0.8~1.2 bar; when P≥4.5 bar, trigger the back pressure valve to release pressure and shut off the heating; monitor the liquid level in the constant pressure tank through an ultrasonic sensor, and automatically replenish the liquid when it is below 1 / 3.

2. The adaptive temperature control method for ultra-low temperature freezers based on fuzzy control according to claim 1, characterized in that, Also includes: Step S5, Dynamic Interference Compensation and Status Monitoring: Dynamically compensate power based on ambient temperature fluctuations, and adjust cooling power accordingly for every 5°C change in ambient temperature; The real-time heat load is calculated based on flow rate and temperature difference. For every 1kW increase in heat load, the cooling power increases by 1.2kW or the heating power decreases by 1.2kW. The system status is monitored in real time. If the temperature deviation exceeds ±1℃ for 5 seconds, a first-level warning is triggered, increasing the adjustment range by 20%. If the pressure exceeds 5 bar or falls below 0.3 bar for 3 seconds, a second-level warning is triggered, immediately shutting down the compressor and heating element and releasing the pressure. If the flow rate is below 0.3L / min for 5 seconds, a level 3 warning will be triggered, the machine will be shut down and an alarm will sound to indicate blockage, and fault information will be recorded at the same time; Step S6: Accumulated Temperature Deviation Prediction and Anticipatory Adjustment. This step captures the cumulative trend of temperature deviation through integral calculations, adjusting temperature control parameters in advance to avoid overshoot caused by the lag in traditional temperature control. It is particularly suitable for scenarios with slowly changing heat loads. The calculation method for the accumulated temperature deviation is as follows: , where T acc Let ΔT(τ) be the cumulative temperature deviation at time t, and ΔT(τ) be the instantaneous temperature deviation at time τ. ΔT(τ) = target temperature - T(τ), where t is the monitoring time period and τ is the integral variable. acc When the temperature exceeds 10℃•s, it is determined that the temperature is consistently too low. Therefore, the heating power adjustment ΔQ is increased by 15% in advance, while the cooling power adjustment ΔP is decreased by 15%. When T... acc When the temperature is less than -10℃•s, it is determined that the temperature is consistently too high. Therefore, the cooling power adjustment ΔP is increased by 15% in advance, while the heating power adjustment ΔQ is decreased by 15% to suppress the temperature rise. When |T acc When |≤10℃•s, maintain the original fuzzy inference result; after adjustment, recalculate T every 5s. acc Dynamically adjust the lead time.

3. The adaptive temperature control method for ultra-low temperature freezers based on fuzzy control according to claim 1, characterized in that, Also includes: Step S7: Precise adjustment of pressure-temperature coupling. Considering the nonlinear coupling relationship between refrigerant temperature and pressure, the target pressure is dynamically set based on the integral of temperature deviation. The formula for setting the target pressure is as follows: , where P set is the current target pressure, P0 is the base pressure, k is the pressure-temperature coupling coefficient, T set is the target temperature, T(τ) is the actual temperature at time τ, t is the adjustment time, and τ is the integration variable.

4. The adaptive temperature control method for ultra-low temperature freezers based on fuzzy control according to claim 1, characterized in that, Also includes: Step S8, refrigerant flow self-adaptive matching, according to real-time temperature deviation and pressure state, dynamically adjust the circulating flow to optimize heat exchange efficiency, avoid excessive flow energy consumption or insufficient heat exchange; First, calculate the flow adjustment coefficient K f , K f =0.5+0.1×|ΔT(τ)|+0.05×(P-P set ), wherein ΔT(τ) is the temperature deviation P is the actual pressure, P set is the target pressure; Then calculate the target flow Q base =Q set ×K base according to the basic flow Q f ; Through the variable frequency circulating pump to realize the flow control, the relationship between speed and flow is n=n base ×(Q set / Q base ), n is the target speed, n base is the basic speed; When |∆T(τ)|>2℃ and K f <1.2, forced K f to 1.2 to speed up heat exchange; When P>P set +0.5bar and K f >0.8, forced K f to 0.8 to reduce the pipeline pressure; After flow adjustment, check through sensor feedback, adjust the speed step 50rpm when the deviation is more than ±0.2L / min, until the standard is reached.

5. The adaptive temperature control method for an ultra-low temperature freezer based on fuzzy control according to claim 1, characterized in that, Also includes: Step S9, monitoring and automatic compensation of the concentration of the coolant, in view of the concentration change caused by volatilization and leakage in the long-term circulation of the 50% ethylene glycol solution, the concentration is maintained stable through online monitoring and compensation to avoid the increase of the freezing point or the decrease of the boiling point; an online refractometer is installed on the coolant return pipeline to measure the concentration C every 30 minutes meas ; when C meas <48%, the compensation process is started: the concentration deviation ΔC=50%-C meas is calculated, the volume of the 99.9% high-concentration ethylene glycol to be supplemented is calculated according to the total volume V total of the system; the high-concentration ethylene glycol is injected at a rate of 0.1 L / min through a metering pump, after the injection, the circulating pump is started to stir for 10 minutes, and the concentration is measured again until 48%≤C meas ≤52%; when C meas >52%, deionized water is supplemented until the concentration meets the standard.

6. The adaptive temperature control method for an ultra-low temperature freezer based on fuzzy control according to claim 1, characterized in that, Also includes: Step S10: Optimize the cooling-heating switching logic. Addressing the equipment wear and temperature control fluctuations caused by frequent switching in traditional methods, a switching strategy is designed based on temperature deviation ranges. ΔT is divided into three ranges: fine adjustment, normal adjustment, and rapid adjustment. The fine adjustment range uses pulse control, with cooling power operating at 30% power for 5 seconds followed by a 5-second shutdown, and heating power operating at 50% power for 3 seconds followed by a 7-second shutdown. The normal adjustment range uses continuous adjustment, adjusting in 50% steps based on the fuzzy inference results. The rapid adjustment range allows for short-term compressor overload and full heating power, with an 8% step for the expansion valve opening to quickly reduce deviations. A 5-second buffer period is set during switching; for example, when switching from rapid to normal, the cooling power decreases to the target value at a rate of 5% / s.

7. The adaptive temperature control method for an ultra-low temperature freezer based on fuzzy control according to claim 1, characterized in that, Also includes: Step S11: Multi-sensor fault diagnosis and backup switching. When the temperature sensor fails, immediately switch to the backup sensor. The fault diagnosis logic is that if the sensor data exceeds the normal measurement range for 5 seconds or the data does not change, it is determined to be a sensor fault, and the controller automatically triggers the switching of the backup sensor. The backup sensor is pre-installed in a pipeline position close to the main sensor. After switching, the validity of the backup sensor data is verified by comparing the correlation between the backup sensor and the pressure and flow data. If the backup sensor data is abnormal, a level 2 warning will be triggered, prompting manual maintenance, while maintaining the current temperature control parameters for 1 minute.

8. The adaptive temperature control method for an ultra-low temperature freezer based on fuzzy control according to claim 1, characterized in that, Also includes: Step S12: Multi-load coordinated temperature control. When the chiller serves 2 to 5 loads, resources are allocated according to heat load and priority. Calculate the real-time heat load of each load; set the load priority weight; the total heat load is the sum of the heat loads of each load, and allocate cooling power and heating power according to the ratio of (load weight × load heat load) / total weight heat load; set stricter temperature control thresholds for high priority loads; when the total heat load exceeds the rated cooling / heating capacity of the chiller, prioritize the operation of high priority loads and reduce the power allocation of low priority loads.

9. The adaptive temperature control method for an ultra-low temperature freezer based on fuzzy control according to claim 1, characterized in that, Also includes: Step S13: Dynamic calibration of constant pressure tank. Regularly calibrate the pressure monitoring value of the constant pressure tank to avoid control deviation caused by pressure sensor drift. The calibration cycle is set to once a week. The calibration process is as follows: Close the connecting valve between the constant pressure tank and the circulation pipeline, and inject pressure into the constant pressure tank through a standard pressure source; record the measured value of the constant pressure tank pressure sensor at each calibration point, and calculate the deviation between the measured value and the standard pressure value; if the deviation exceeds ±0.02 bar, correct the sensor measurement coefficient through the central controller; after calibration, reopen the connecting valve between the constant pressure tank and the circulation pipeline to restore normal operation.

10. The adaptive temperature control method for an ultra-low temperature freezer based on fuzzy control according to claim 1, characterized in that, Also includes: Step S14: Dynamically correct the fuzzy control rule base, periodically analyze temperature control data, optimize fuzzy control rules, and improve long-term temperature control stability; The correction cycle is set to once a month, and the correction process is as follows: extract more than 1,000 sets of valid temperature control data for the current month; calculate the number of times each fuzzy rule is executed and the average value of the corresponding temperature control deviation; if the absolute value of the average temperature deviation of a certain rule exceeds 0.3℃ and the number of executions exceeds 50, correct the output adjustment of the rule; at the same time, add or delete rules that are adapted to specific scenarios according to the temperature control characteristics of the load type.

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