Simulation of temperature control in large-scale high and low temperature environmental laboratory

doi:10.11949/0438-1157.20250110

Abstract

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

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

CLC Number:

TQ 028.8

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.

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.

Figures/Tables 22

Fig.1 Schematic diagram of the laboratory

Fig.2 Schematic diagram of temperature controlling

Fig.3 The Simulink model of temperature controlling system

Fig.4 Simplified shell and tube heat exchanger

Fig.5 The Simulink model of heat exchanger subsystem

Fig.6 The Simulink model of enclosure structure

Fig.7 The Simulink model of icing system

Fig.8 The Simulink model of temperature inside the room

Fig.9 Laboratory control flowchart

Fig.10 The Simulink model of control system

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

Fig.12 Configuration for DXP data monitoring

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

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

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

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

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

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

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

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

Fig.17 Simulink modeling of a fuzzy PID controller

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

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