Multi-target cooperative temperature control system for closed test container

Abstract

The invention discloses a multi-target cooperative temperature control system and method for a closed test container, and belongs to the technical field of precise temperature control. The system comprises a closed container, a plurality of temperature control units arranged in the container, a temperature sensing network used for detecting the temperature of each temperature control unit and the environment temperature, a central processing unit and a cooperative control module. And the cooperative control module dynamically coordinates the working states of the global intracavity temperature control unit and the local fine-tuning temperature control units and performs cooperative control based on a multivariable prediction control algorithm according to the set temperature and the actually measured temperature of each temperature control unit, so as to decouple the thermal coupling effect between the temperature control units, and control the temperature control units. Therefore, the temperature in the cavity of the closed test container can reach a dynamic balance. The device effectively solves the problem of mutual interference during multi-target independent temperature control in a closed test container, realizes high-precision and high-stability cooperative temperature control, and is particularly suitable for parallel experiments and tests in the fields of biological pharmacy, material science and the like.

Description

Technical Field

[0001] This invention relates to the fields of microelectronics, biomedicine, and materials chemistry. Specifically, it describes a method for achieving precise, stable, and coordinated temperature control of multiple independent units within a sealed test container (such as a biological incubator, materials heat treatment equipment, or electronic test chamber) sharing a thermal environment. This means that, with the system insulated from the external environment, the test container contains multiple objects and areas requiring temperature control. By independently setting and adjusting the temperature of each target area, there is no interference between them. When the temperature of one target changes, the temperature impact on other targets should be minimized, achieving a dynamic temperature balance within the controlled object, thereby effectively increasing the lifespan of the equipment. Background Technology

[0002] In industrial fields such as biopharmaceuticals, materials science, and electronic testing, it is often necessary to precisely control the temperature of multiple samples or targets simultaneously within the same sealed container. For example, in biopharmaceuticals, different cell lines need to be cultured at different temperatures in the same incubator; in materials science, different material samples need to be heat-treated at different temperatures in the same heat treatment equipment; and in electronic testing, multiple electronic modules need to be subjected to different temperature stresses within the same test chamber. While traditional single-target temperature control systems are technologically mature, they cannot meet the needs of multi-target parallel temperature control. Existing multi-target temperature control solutions mainly suffer from the following technical shortcomings: First, the thermal coupling effect within a confined container is extremely severe. Due to space constraints, strong thermal coupling occurs between targets through thermal radiation, convection, and conduction. When multiple independent traditional PID controllers are used, severe interference arises between the control loops, leading to system oscillation, overshoot, and even instability. Existing technologies are insufficient in handling the non-uniformity of the spatial thermal field. Traditional control strategies, such as PID control, are "lag-response" control, unable to predict the future behavior of the system, let alone actively compensate for the coupling effect between multiple targets. When the temperature of one target changes, it will cause temperature shocks to other targets, requiring a long time to regain stability. Furthermore, existing systems perform poorly in terms of energy efficiency. Simultaneous cooling and heating often occur, resulting in energy waste. Therefore, there is an urgent need in this field for a novel temperature control system that can effectively decouple thermal interference, achieve independent high-precision temperature control for multiple targets, and is highly energy efficient. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-objective collaborative temperature control system and method for sealed containers, so as to overcome the shortcomings of the prior art, effectively solve the problem of strong coupling interference in multi-objective temperature control in sealed environments, and realize multi-objective independent, accurate, stable and efficient collaborative temperature control.

[0004] The present invention adopts the following technical solution: A multi-target collaborative temperature control system for a sealed container includes: a sealed container made of insulating material to ensure thermal insulation against the external environment; multiple temperature control units disposed within the sealed container for regulating the temperature of their respective targets; a temperature sensing network for detecting the actual temperature of each target and the internal ambient temperature of the sealed container; and a collaborative controller communicatively connected to the temperature sensing network and all temperature control units.

[0005] The coordinated controller is configured to generate a set of control instructions for coordinating the control of all temperature control units based on a multivariable predictive control model, according to the set temperature of each target and the actual temperature of each target, so that the actual temperature of each target approaches its set temperature and the thermal interference between the temperature control units is suppressed.

[0006] The temperature control unit includes: (1) A global background temperature control subsystem, used to adjust the base temperature of the internal environment of the sealed container; (2) Multiple local fine-tuning temperature control subsystems, each corresponding to one of the aforementioned targets, are used for fine-tuning the base temperature. Each local fine-tuning temperature control subsystem is a thermoelectric cooler capable of both heating and cooling, with fast response and high control accuracy. The co-controller is configured to control the internal ambient temperature near the average of all target set temperatures to minimize the workload of the local fine-tuning temperature control subsystems.

[0007] The present invention also provides a control method for multi-objective coordinated temperature control of a sealed container, comprising the following steps: (1) Obtain the set temperature and actual temperature of each target inside the sealed container; (2) Input the set temperature and the actual temperature into the pre-established multivariate predictive control model; (3) Using the multivariate predictive control model, predict the changing trend of each target temperature in the future period of time, and solve for a set of optimal control variables; (4) Generate coordinated control commands based on the optimal control quantity and send them to the corresponding temperature control actuators to drive each temperature control actuator to perform actions simultaneously and achieve coordinated temperature control.

[0008] Compared with the prior art, the present invention has the following significant advantages: (1) By using a multivariable predictive control algorithm, the thermal coupling effect between targets can be actively predicted and compensated, which fundamentally solves the mutual interference problem in multi-target temperature control and significantly improves the control accuracy and stability of the system. Experiments show that the present invention can stabilize the temperature control accuracy within ±0.1°C, which is far superior to ±0.5°C of the traditional method.

[0009] (2) By combining global background temperature control with local fine-tuning, the power requirements of local actuators are greatly reduced, the system energy efficiency is improved, and the energy waste caused by the conflict between cooling and heating is avoided.

[0010] (3) The present invention adopts a rolling optimization and feedback correction mechanism, which can adapt to the dynamic changes of the system and external disturbances, and has strong robustness. It can still maintain excellent control performance when the target heat capacity changes or the external environment fluctuates. Attached Figure Description

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0012] Figure 1 This is a schematic diagram of the system of the present invention.

[0013] Figure 2 This is a schematic diagram of the control system of the present invention.

[0014] Figure 3 This is a flowchart of the multivariate predictive control algorithm of the present invention. Detailed Implementation

[0015] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] Please see Figure 1 This article uses an incubator with three independent temperature control units as an example for illustration.

[0017] The sealed container 1 has a 304 stainless steel inner liner and an outer cold-rolled steel plate, with 50mm thick polyurethane foam insulation material filling the middle to ensure good thermal insulation performance. The effective internal volume of the container is 100L, and it has three independent sample stations. The container door has a double-layer hollow glass observation window and uses a magnetic sealing strip to ensure airtightness.

[0018] The temperature control unit comprises a global background temperature control subsystem 2 and three local fine-tuning temperature control subsystems 3. The global background temperature control subsystem 2 includes a compressor refrigeration unit 21 and a set of nickel-chromium alloy electric heating wires 22, located on the rear wall and bottom of the container, respectively. The refrigeration unit 21 has a rated power of 800W and uses environmentally friendly refrigerant R134a; the heating wires 22 have a total power of 1000W and are controlled by a PID solid-state relay. Each local fine-tuning temperature control subsystem 3 includes a thermoelectric cooling element 31 (TEC, model TEC1-12706, maximum cooling power 70W) and an aluminum heat sink 32 attached to its hot side. The cold side of the TEC 31 is in close contact with the target sample tray 4 via high thermal conductivity silicone grease, ensuring efficient heat transfer. Each TEC is equipped with an independent H-bridge drive circuit, enabling bidirectional control of heating and cooling.

[0019] The temperature sensing network 5 includes: an ambient PT100 sensor 51 (accuracy ±0.1°C) for detecting the ambient temperature inside the container, suspended in the center of the container; and three high-precision PT100 sensors 52 (accuracy ±0.05°C) for directly measuring the target temperature, which are embedded in the bottom of the three sample pans 4 and in direct contact with the sample.

[0020] The Cooperative Controller 6 uses an industrial computer (IPC, model: Advantech UNO-2484G) as its core, equipped with an Intel Core i5 processor, 8GB of memory, and a 128GB solid-state drive. It acquires signals from all PT100 sensors via a 16-channel analog input module (model: Advantech PCI-1716); and outputs control signals to the TEC's drive power supply and the solid-state relays of the global temperature control system via an 8-channel analog output module (model: Advantech PCI-1723).

[0021] The human-machine interface uses a 10-inch touch screen and runs self-developed monitoring software, which can display the temperature curves of each target in real time, set control parameters, record historical data and alarm information.

[0022] The specific steps for establishing a predictive model for the system are as follows: 1. System initialization: Stabilize the system at the initial equilibrium point (25°C) and keep all actuator power constant for at least 2 hours to ensure the system is completely stable.

[0023] 2. Coupling test: Keep the power of the global temperature control and other TECs constant, and apply a 15% power step signal (from 0% to 15%) to the first TEC; continuously record the temperature change data of the three target sample plates over the next 600 seconds with a sampling period of 1 second, until the system stabilizes again; repeat the above test for the second and third TECs and the global heating / cooling unit in sequence.

[0024] 3. Data preprocessing: The collected step response data is filtered to eliminate the influence of measurement noise.

[0025] 4. Model Extraction: The least squares method is used to fit the processed data to obtain the dynamic matrix A of the system. This matrix is ​​4×3 (4 actuators, 3 temperature outputs), and each element a_ij represents the influence coefficient of the unit step input of the j-th actuator on the i-th temperature output at a specific time.

[0026] See Figure 3 The collaborative controller 6 executes the following algorithm flow in each control cycle (1 second): (1) Read the current values. Read the current actual temperature value T_actual(k) and ambient temperature of all targets from the sensor.

[0027] (2) Feedback correction. Compare T_actual(k) with the predicted value T_predicted(k|k-1) at the previous time step to obtain the prediction error e(k) = T_actual(k) - T_predicted(k|k-1). Use this error to correct future predictions to overcome model mismatch and external disturbances.

[0028] (3) Rolling optimization. This is the core step of the algorithm, including: 1. Set the optimization time domain: prediction time domain N_p=60 (i.e., predict the next 60 seconds), control time domain N_c=20 (i.e., calculate the control quantity for the next 20 steps).

[0029] 2. Construct the objective function: Minj=Σ_{i=1}^{N_p}[(T_setpoint(k+i)-T_predicted(k+i))^T·Q(T_setpoint(k+i)-T_predicted(k+i))]+Σ_{i=0}^{N_c-1}[ΔU(k+i)^T·R·ΔU(k+i)].

[0030] Where Q is a 3×3 output error weight matrix, which is a diagonal matrix diag([1.0, 1.0, 1.0]), indicating that the tracking accuracy of the three targets is given equal importance; R is a 4×4 control increment weight matrix, which is a diagonal matrix diag([0.1, 0.1, 0.1, 0.05]), appropriately relaxing the action restrictions on the global temperature control system.

[0031] 3. Solving the optimization problem: The above-mentioned constrained optimization problem is transformed into a quadratic programming problem. The embedded QP solver (using the QP oases library in C++) is called to solve the problem and obtain the optimal control increment sequence ΔU for the next N_c steps.

[0032] 4. Implement control. Take the first control increment ΔU*(0) in the optimal sequence, calculate the actual control quantity U(k) = U(k-1) + ΔU*(0) at the current moment, and send it to each actuator through the output module.

[0033] 5. Loop. k=k+1, return to (1), and enter the next control cycle.

[0034] After startup, the system of this invention (solid line) smoothly reaches the set values ​​for each target temperature without overshoot, with adjustment times of T1-320s, T2-280s, and T3-450s, respectively. In contrast, the traditional PID system (dashed line) exhibits significant oscillations and overshoot, with adjustment times extended by more than 40%, and the heating process in T1 causes significant temperature shocks to T2 and T3 (maximum coupling reaching 2.5°C). Throughout the steady-state operation, this invention maintains temperature control accuracy within ±0.1°C, with a coupling degree less than 0.2°C; while the control accuracy of the traditional PID method is only ±0.5°C, with a coupling degree exceeding 1.0°C. Regarding energy consumption, the global + local temperature control architecture of this invention reduces the average power consumption of the system by approximately 25% compared to traditional methods, with particularly significant energy-saving effects under conditions with large temperature ranges.

[0035] The specific implementation methods described above provide a detailed explanation of the purpose, technical solution, and beneficial effects of this invention. It should be understood that the above description is merely a specific implementation method of this invention and is not intended to limit the scope of protection of this invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A multi-objective coordinated temperature control system for a sealed container, comprising: The sealed container is made of insulating material to ensure heat insulation from the external environment; Multiple temperature control units are disposed within the sealed container to regulate the temperature of their respective targets; a temperature sensing network is used to detect the actual temperature of each target and the internal ambient temperature of the sealed container; a coordination controller is communicatively connected to the temperature sensing network and all temperature control units. The coordinated controller is configured to generate a set of control instructions for coordinating the control of all temperature control units based on a multivariable predictive control model, according to the set temperature of each target and the actual temperature of each target, so that the actual temperature of each target approaches its set temperature and the thermal interference between the temperature control units is suppressed.

2. The system according to claim 1, characterized in that, The temperature control unit includes: a global background temperature control subsystem for adjusting the base temperature of the internal environment of the sealed container; and multiple local fine-tuning temperature control subsystems, each of which corresponds to a target and is used to fine-tune the base temperature.

3. The system according to claim 2, characterized in that, The local fine-tuning temperature control subsystem is a thermoelectric cooling element.

4. The system according to claim 1, characterized in that... The method for establishing the multivariable predictive control model includes obtaining the transfer function matrix characterizing the influence of each temperature control unit on each target temperature through step response testing.

5. The system according to claim 1, characterized in that, The co-controller is also configured to control the internal ambient temperature to be close to the average of all target set temperatures.

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