Scholarlink

<p>The transition toward sustainable energy systems necessitates efficient and environmentally friendly cooling solutions, as conventional refrigeration contributes significantly to greenhouse gas emissions. Absorption refrigeration machines offer a promising alternative, particularly when driven by renewable sources like solar energy. However, optimizing their performance, especially chiller outlet temperature regulation, remains a critical challenge for wider adoption. To address this issue, this paper proposes and evaluates a novel application of bio-inspired optimization algorithms for tuning Proportional–Integral (PI) controllers in a BROAD-BZH&#xa0;15 double-effect solar absorption machine. Particle Swarm Optimization (PSO), Differential Evolution (DE), Grasshopper Optimization Algorithm (GOA), and Moth–Flame Optimization (MFO) are systematically investigated and compared across three operating points. The main contribution of this study is a comprehensive comparative framework that evaluates the proposed bio-inspired PI controllers against both an adaptive Proportional–Integral Fuzzy (PI-Fuzzy) controller and a conventionally tuned PI controller based on affine parameterization (PI-AP), using performance metrics, such as OS, settling time, and steady-state error. Experimental results demonstrate that the proposed methods achieve the shortest settling time, with a value of 2&#xa0;s, compared with 11.49&#xa0;s for the PI-Fuzzy controller and 7.23&#xa0;s for the PI-AP controller at the first evaluated operating point. However, the proposed approach exhibits a higher overshoot of 4.11%, whereas both PI-AP and PI-Fuzzy present zero overshoot. At the second evaluated operating point, the proposed methods again achieve the shortest settling time, with a value of 2.64&#xa0;s, compared with 13.90&#xa0;s for the PI-Fuzzy controller and 16.31&#xa0;s for the PI-AP controller. In this case, all controllers exhibit zero overshoot, indicating stable and well-damped responses. Despite this trade-off, the proposed controllers consistently achieve zero steady-state error under all tested conditions, confirming their effectiveness and reliability. Although the PI-Fuzzy and affine-tuned PI controllers achieve slightly better overall performance, the bio-inspired controllers provide faster stabilization and simpler implementation. Furthermore, robustness analysis under measurement noise with a power level of 20% demonstrates stable and reliable performance of the proposed controllers. These results highlight the effectiveness of bio-inspired optimization techniques as practical and competitive solutions for enhancing the control performance of solar-powered absorption refrigeration systems.</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

Control of a double-effect absorption machine in a solar refrigeration plant using adaptive and bio-inspired control

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

查看全文
  • Mario A Pastrana Triana,
  • Jose A Mendoza Peñaloza,
  • Gabriel Triana Osorio,
  • Lukas A Da Silva,
  • Daniel Mauricio Muñoz Arboleda,
  • Juan Escaño

摘要

The transition toward sustainable energy systems necessitates efficient and environmentally friendly cooling solutions, as conventional refrigeration contributes significantly to greenhouse gas emissions. Absorption refrigeration machines offer a promising alternative, particularly when driven by renewable sources like solar energy. However, optimizing their performance, especially chiller outlet temperature regulation, remains a critical challenge for wider adoption. To address this issue, this paper proposes and evaluates a novel application of bio-inspired optimization algorithms for tuning Proportional–Integral (PI) controllers in a BROAD-BZH 15 double-effect solar absorption machine. Particle Swarm Optimization (PSO), Differential Evolution (DE), Grasshopper Optimization Algorithm (GOA), and Moth–Flame Optimization (MFO) are systematically investigated and compared across three operating points. The main contribution of this study is a comprehensive comparative framework that evaluates the proposed bio-inspired PI controllers against both an adaptive Proportional–Integral Fuzzy (PI-Fuzzy) controller and a conventionally tuned PI controller based on affine parameterization (PI-AP), using performance metrics, such as OS, settling time, and steady-state error. Experimental results demonstrate that the proposed methods achieve the shortest settling time, with a value of 2 s, compared with 11.49 s for the PI-Fuzzy controller and 7.23 s for the PI-AP controller at the first evaluated operating point. However, the proposed approach exhibits a higher overshoot of 4.11%, whereas both PI-AP and PI-Fuzzy present zero overshoot. At the second evaluated operating point, the proposed methods again achieve the shortest settling time, with a value of 2.64 s, compared with 13.90 s for the PI-Fuzzy controller and 16.31 s for the PI-AP controller. In this case, all controllers exhibit zero overshoot, indicating stable and well-damped responses. Despite this trade-off, the proposed controllers consistently achieve zero steady-state error under all tested conditions, confirming their effectiveness and reliability. Although the PI-Fuzzy and affine-tuned PI controllers achieve slightly better overall performance, the bio-inspired controllers provide faster stabilization and simpler implementation. Furthermore, robustness analysis under measurement noise with a power level of 20% demonstrates stable and reliable performance of the proposed controllers. These results highlight the effectiveness of bio-inspired optimization techniques as practical and competitive solutions for enhancing the control performance of solar-powered absorption refrigeration systems.