Energy-Saving Control Study of R507A/CO<sub>2</sub> Composite Refrigeration System based on Adaptive-Augmented Particle Swarm Optimization Algorithm

CHEN Wanting; YAO Ye; QU Haiyan; CUI Bo

doi:10.7652/xjtuxb202505009

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Energy-Saving Control Study of R507A/CO₂ Composite Refrigeration System based on Adaptive-Augmented Particle Swarm Optimization Algorithm

更新时间：2025-08-26

- Energy-Saving Control Study of R507A/CO₂ Composite Refrigeration System based on Adaptive-Augmented Particle Swarm Optimization Algorithm
- Journal of Xi’an Jiaotong University Vol. 59, Issue 5, Pages: 87-96(2025)
- 作者机构：
  
  1.上海交通大学机械与动力工程学院， 200240，上海
  2.济南大森制冷科技有限公司， 250000，济南
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202505009
  CLC： TB61;TB657.5;TP311
- Received：05 September 2024，
  
  Online First：09 January 2025，
  
  Published：10 May 2025
- 稿件说明：
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CHEN Wanting, YAO Ye, QU Haiyan, et al. Energy-Saving Control Study of R507A/CO₂ Composite Refrigeration System based on Adaptive-Augmented Particle Swarm Optimization Algorithm[J]. Journal of Xi’an Jiaotong University, 2025, 59(5): 87-96. DOI: 10.7652/xjtuxb202505009.
DOI：

CHEN Wanting, YAO Ye, QU Haiyan, et al. Energy-Saving Control Study of R507A/CO₂ Composite Refrigeration System based on Adaptive-Augmented Particle Swarm Optimization Algorithm[J]. Journal of Xi’an Jiaotong University, 2025, 59(5): 87-96. DOI: 10.7652/xjtuxb202505009. DOI：

摘要

针对冷库制冷系统中传统控制策略导致的能耗高、食品质量受损和不适应变负荷工况的问题，提出了基于自适应增强粒子群优化（AAPSO）算法的R507A/CO

复合制冷系统全局优化节能控制策略。首先，综合考虑了压缩机、冷却塔和风机的耦合关系，建立了以能耗最小为目标的冷库制冷系统全局优化控制模型；然后，通过对群体学习因子和个体学习因子进行自适应改进，提出了一种具有动态调节能力的自适应增强粒子群优化算法，以应对冷库制冷系统的动态负荷变化需求；最后，将所提出的AAPSO算法应用于浙江省某冷库制冷系统的节能优化控制，开展节能优化实验。结果表明：AAPSO算法较粒子群优化（PSO）算法有更高的鲁棒性、动态适应性和求解效率；与传统的规则控制策略相比，基于AAPSO算法优化后的冷库系统在稳定工况下全段平均节能率达到3.5%，在非稳定工况下全段平均节能率达到9.5%，证明了基于AAPSO算法的全局优化节能控制策略的有效性，可为冷库系统节能运行提供理论依据和实践参考。

Abstract

To address the issues of high energy consumption

compromised food quality

and inability to adapt to variable load working conditions caused by traditional control strategies in cold storage refrigeration systems

a global energy-saving optimization control strategy for the R507A/CO

composite refrigeration system based on the adaptive-augmented particle swarm optimization (AAPSO) algorithm is proposed. First

the coupling relationships among the compressor

cooling tower

and fans are comprehensively considered

and a global optimization control model for the cold storage refrigeration system is established to minimize energy consumption. Then

by adaptively improving the group learning factor and individual learning factor

a dynamically adjustable AAPSO algorith

m is developed to address the dynamic load variation requirements of cold storage refrigeration systems. Finally

the proposed AAPSO algorithm is applied to the energy-saving optimization control of a refrigeration system in a cold storage facility in Zhejiang

China

and energy-saving optimization experiments were conducted. The results show that AAPSO algorithm outperforms PSO algorithm in terms of robustness

dynamic adaptability

and solution efficiency. Compared with traditional rule-based control strategies

the cold storage system optimized based on the AAPSO algorithm achieves an average energy-saving rate of 3.5% across the entire load range under stable operating conditions and 9.5% under non-stable operating conditions. These findings demonstrate the effectiveness of the global energy-saving optimization control strategy based on AAPSO algorithm

providing theoretical guidance and practical reference for the energy-efficient operation of cold storage systems.

关键词

Keywords

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Energy-Saving Control Study of R507A/CO2 Composite Refrigeration System based on Adaptive-Augmented Particle Swarm Optimization Algorithm

DOI：10.7652/xjtuxb202505009

摘要

Abstract

关键词

Keywords

references

Energy-Saving Control Study of R507A/CO₂ Composite Refrigeration System based on Adaptive-Augmented Particle Swarm Optimization Algorithm