基于模糊逻辑的预测和参数优化,使用粒子群优化来提高金字塔太阳能静态中的性能
N Senthilkumar1, M Yuvaperiyasamy2, B Deepanraj3
1Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu 602105, India.
概括
这项研究利用模糊逻辑和粒子群优化优化了金字塔太阳静止 (PSS) 性能. 该模型通过在不同的环境条件和纳米颗粒度下确定最佳参数来提高PSS生产率.
科学领域:
- 可再生能源工程可再生能源工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 太阳能静止灯对于淡水生产至关重要,但它们的效率往往受到环境因素和材料性能的限制.
- 像含有银纳米粒子 (Ag) 的石等相变材料 (PCM) 可以改善太阳能设备中的热储和热传输.
- 优化太阳能静止参数是复杂的,因为许多相互作用的变量和系统的不确定性.
研究的目的:
- 开发一个强大的模型来预测金字塔太阳能静止器 (PSS) 的最佳参数.
- 通过优化太阳能强度,水深和PCM中的银纳米粒子度来提高PSS的生产率 (P).
- 使用模糊推理系统 (FIS) 尽量减少系统不确定性,并通过粒子群优化 (PSO) 微调设置.
主要方法:
- 利用塔古奇的L9直角阵列进行实验设计.
- 通过与理想解决方案的相似性排序偏好 (TOPSIS) 用于过程参数优化.
- 集成的模糊逻辑接口 (FL) 和粒子群优化 (PSO) 用于预测最佳的PSS操作条件.
主要成果:
- 确定了太阳能强度 (350-950W/m2),水深 (4-8厘米) 和Ag纳米粒子度 (0.5-1.5%) 的最佳范围.
- 证明了该模型能够预测最佳参数以提高PSS生产力 (P),玻璃温度 (Tg) 和盆地水温 (Tw).
- 通过使用FIS和PSO成功降低了系统不确定性和微调参数.
结论:
- 开发的FL-PSO模型为优化PSS性能提供了一种有效的方法.
- 该研究强调了太阳能强度,水深和Ag-PCM度对PSS生产力的重大影响.
- 综合优化技术提供了一条改善太阳能静止炉淡水发电效率的途径.
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