大型高低温环境实验室温度控制仿真

doi:10.11949/0438-1157.20250110

摘要/Abstract

摘要：

针对大型高低温环境实验室的温度控制问题，提出了一种基于Matlab/Simulink的仿真方法。使用集总参数法简化并建立了实验室的模型，综合分析热容、热阻、热源和热损失等因素，通过调节制冷系统的压缩机开启比例、电加热器的功率及覆冰和除冰系统的状态，实现精确的温度控制并探索换热器预冷和覆冰系统接入时间对温度平衡及能耗的影响。研究发现，在不同的温度设定下，于PID拐点接入覆冰系统的温度调控时间依然最短，强化了该策略的应用价值；此外，引入模糊PID控制策略后，模糊PID控制在响应速度和超调抑制方面表现更为优越。

关键词: 高低温环境实验室, 温度控制, Simulink仿真, 能耗优化, 组态开发

Abstract:

This paper proposes a Matlab/Simulink-based simulation method for temperature control in large-scale high and low temperature environmental laboratories. The laboratory model is simplified and established using the lumped parameter method, integrating factors such as thermal capacity, thermal resistance, heat sources, and heat losses. By adjusting the compressor operation ratio of the cooling system, the power of electric heaters, and the status of icing and de-icing systems, precise temperature control is achieved. The research findings indicate that, under various temperature settings, the temperature regulation time remains the shortest when the icing system is engaged at the PID inflection point, reinforcing the application value of this strategy. Furthermore, after introducing the fuzzy PID control strategy, fuzzy PID control exhibits superior performance in terms of response speed and overshoot suppression.

Key words: high and low-temperature environmental laboratory, temperature control, Simulink simulation, energy consumption optimization, configuration development

中图分类号:

TQ 028.8

石一帆, 柯钢, 陈浩, 黄孝胜, 叶芳, 李成娇, 郭航. 大型高低温环境实验室温度控制仿真[J]. 化工学报, 2025, 76(S1): 268-280.

Yifan SHI, Gang KE, Hao CHEN, Xiaosheng HUANG, Fang YE, Chengjiao LI, Hang GUO. Simulation of temperature control in large-scale high and low temperature environmental laboratory[J]. CIESC Journal, 2025, 76(S1): 268-280.

图/表 22

图1 实验室工作原理流程图1—锅炉;2—水箱;3—纯水设备;4—冷水机组;5—高压泵组;6—空压机组;7—喷淋末端;8—融冰灯;9—风机箱;10—冷凝器

Fig.1 Schematic diagram of the laboratory

图2 温度控制系统示意图

Fig.2 Schematic diagram of temperature controlling

图3 温度控制系统Simulink模型

Fig.3 The Simulink model of temperature controlling system

图4 简化的管壳式换热器

Fig.4 Simplified shell and tube heat exchanger

图5 换热器子系统Simulink模型

Fig.5 The Simulink model of heat exchanger subsystem

图6 围护结构Simulink模型

Fig.6 The Simulink model of enclosure structure

图7 覆冰系统Simulink模型

Fig.7 The Simulink model of icing system

图8 室内温度综合Simulink模型

Fig.8 The Simulink model of temperature inside the room

图9 实验室控制流程图

Fig.9 Laboratory control flowchart

图10 控制系统Simulink模型

Fig.10 The Simulink model of control system

图11 MCGS组态环境图形界面

Fig.11 The graphical user interface of the MCGS configuration environment

图12 DXP数据监控配置

Fig.12 Configuration for DXP data monitoring

图13 换热器管壁预冷温度曲线

Fig.13 Temperature profile for pre-cooling the tube wall of a heat exchanger

表1 不同预冷温度下温度超调量变化

Table 1 Variations in temperature overshoot at different precooling temperatures

预冷温度/℃	最低温度/℃	超调量/℃
-60	-64.09	9.09
-45	-64.12	9.12
-30	-64.14	9.14
-15	-64.16	9.16
0	-64.18	9.18
15	-64.21	9.21
不预冷	-64.23	9.23

表2 不同预冷温度下实验室温度调控时间

Table 2 Laboratory temperature regulation time at different precooling temperatures

预冷温度/℃	预冷时间/s	平衡时间/s	总时间/s
-60	27.17	45154.07	45181.24
-45	14.09	45167.14	45181.23
-30	8.88	45180.26	45189.14
-15	5.56	45193.32	45198.88
0	3.08	45206.47	45209.55
15	1.15	45219.58	45220.73
不预冷	0	45232.65	45232.65

图14 不同预冷温度下的温度超调量变化

Fig.14 Variation of temperature overshoot at different pre-cooling temperatures

图15 不同预冷温度下实验室降温总时间

Fig.15 Total cooling time in the laboratory at different pre-cooling temperatures

表3 不同工况下覆冰系统接入时间对系统能耗的影响

Table 3 Impact of icing system engagement time on system energy consumption under different operating conditions

工况/℃	接入时间/s	平衡时间/s	累计开度
30~-55	1700	47030	9060.96
	1800	47020	9060.90
	1870	47020	9060.88
	1890	47020	9060.89
	1900	47021	9060.93
30~-25	300	45480	5989.72
	400	45460	5988.66
	420	45460	5988.64
	440	45460	5988.66
	540	45480	5989.85
0~-55	340	44410	7218.13
	440	44400	7216.75
	460	44400	7216.74
	480	45460	7216.76
	580	44480	7218.56

图16 不同工况下覆冰系统接入时间对系统能效的影响

Fig.16 Impact of icing system engagement time on system energy efficiency under different operating conditions

表4 模糊控制规则

Table 4 Fuzzy control rule

e	ec
e	NB	N	ZE	P	PB
NB	B MS S	B MS S	MB S MS	S MS S	MS MB S
N	B B MB	MB MB MB	MS MS MS	S MB MS	S B MB
ZE	MB B B	MS MB MS	S S S	MS MB MS	MB B B
P	S B MB	S MB MS	MS MS MS	MB MB MB	B B MB
PB	MS MB S	S MS S	MB S MS	B MS S	B MS S

图17 模糊PID控制器Simulink建模

Fig.17 Simulink modeling of a fuzzy PID controller

图18 模糊PID与传统PID控制对比

Fig.18 Comparison between fuzzy PID control and traditional PID control

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