Scholarlink

<p>This study focuses on the optimization of double-effect refrigeration cycles by comparing classic and hybrid systems using particle swarm optimization (PSO)<i>.</i> The objective is to evaluate the performance improvements of a hybrid cycle over a classic cycle and demonstrate the effectiveness of the PSO algorithm in achieving optimal outcomes. The methodology involves developing and applying a PSO-based approach to optimize critical decision variables, such as generator temperature and pressure ratio, for both cycle configurations. The results reveal that the hybrid cycle outperforms the classic cycle, with a performance enhancement exceeding 16% while also reducing electrical energy consumption. Notably, the hybrid system achieves a refrigeration capacity of 300 kW with an electrical power input of only 22.759 kW, representing a fourfold improvement over traditional compression system. The PSO approach, with its robust parallel processing capabilities, reduces computational time, and effectively manages the trade-off between generator temperature and pressure ratio. Overall, the study highlights the substantial benefits of integrating a hybrid system with optimized parameters and underscores the efficiency of the PSO methodology in enhancing system performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Particle Swarm Optimization Approach of Hybrid Refrigeration Systems for Steady Low-Temperature Operation

  • 期刊论文

  • 发表日期：

    2025年5月29日

查看全文
  • Mohamed Mmadi Hassane,
  • Yousra Filali Baba,
  • Ahmed Al Mers,
  • Noureddine Boutammachte,
  • Wissal Taibi

摘要

This study focuses on the optimization of double-effect refrigeration cycles by comparing classic and hybrid systems using particle swarm optimization (PSO). The objective is to evaluate the performance improvements of a hybrid cycle over a classic cycle and demonstrate the effectiveness of the PSO algorithm in achieving optimal outcomes. The methodology involves developing and applying a PSO-based approach to optimize critical decision variables, such as generator temperature and pressure ratio, for both cycle configurations. The results reveal that the hybrid cycle outperforms the classic cycle, with a performance enhancement exceeding 16% while also reducing electrical energy consumption. Notably, the hybrid system achieves a refrigeration capacity of 300 kW with an electrical power input of only 22.759 kW, representing a fourfold improvement over traditional compression system. The PSO approach, with its robust parallel processing capabilities, reduces computational time, and effectively manages the trade-off between generator temperature and pressure ratio. Overall, the study highlights the substantial benefits of integrating a hybrid system with optimized parameters and underscores the efficiency of the PSO methodology in enhancing system performance.