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基于改进BOA-PID的LT-MED系统冷凝器出口海水温度控制
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作者 梅世龙 刘擘 鲍克勤 《上海电力大学学报》 CAS 2024年第5期415-420,共6页
针对低温多效蒸馏(LT-MED)海水淡化系统中冷凝器出口海水温度控制问题,在蝴蝶优化算法(BOA)的基础上,提出了一种改进BOA-PID控制方法,并利用MATLAB软件搭建仿真模型,将改进BOAPID控制的控制效果与BOA-PID控制、常规PID控制进行对比。结... 针对低温多效蒸馏(LT-MED)海水淡化系统中冷凝器出口海水温度控制问题,在蝴蝶优化算法(BOA)的基础上,提出了一种改进BOA-PID控制方法,并利用MATLAB软件搭建仿真模型,将改进BOAPID控制的控制效果与BOA-PID控制、常规PID控制进行对比。结果表明,改进BOA-PID在适应度值和全局搜索能力方面表现卓越;与常规PID控制相比,基于改进BOA-PID控制系统中相应的优化程序能够自动整定各个参数,提升了系统的响应速度,大幅减小了超调量,提升了海水淡化系统的造水比,系统能正常运行。 展开更多
关键词 低温多效蒸馏 冷凝器 海水温度控制 比例积分微分控制 蝴蝶优化算法
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The REMO Ocean Data Assimilation System into HYCOM(RODAS_H):General Description and Preliminary Results 被引量:1
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作者 Clemente Augusto Souza TANAJURA Alex Novaes SANTANA +3 位作者 Davi MIGNAC Leonardo Nascimento LIMA Konstantin BELYAEV XIE Ji-Ping 《Atmospheric and Oceanic Science Letters》 CSCD 2014年第5期464-470,共7页
The first version of the Brazilian Oceano- graphic Modeling and Observation Network (REMO) ocean data assimilation system into the Hybrid Coordi- nate Ocean Model (HYCOM) (RODAS H) has recently been constructed ... The first version of the Brazilian Oceano- graphic Modeling and Observation Network (REMO) ocean data assimilation system into the Hybrid Coordi- nate Ocean Model (HYCOM) (RODAS H) has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation (EnOI) scheme and considers the high fre- quency variability of the model error co-variance matrix. The EnOl can assimilate sea surface temperature (SST), satellite along-track and gridded sea level anomalies (SLA), and vertical profiles of temperature (T) and salinity (S) from Argo. The first observing system experiment was carried out over the Atlantic Ocean (78°S-50°N, 100°W-20°E) with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integra- tions were performed, including the control run without assimilation. In the other four, different observations were assimilated: SST only (A SST); Argo T-S profiles only (AArgo); along-track SLA only (A_SLA); and all data employed in the previous runs (A_All). The A_SST, A_Argo, and A_SLA runs were very effective in improv- ing the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to ob- servations. Overall, the A_All run produced a good analy- sis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%. 展开更多
关键词 ocean data assimilation ensemble optimalinterpolation observing system experiment HYCOM Atlantic Ocean
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