Data-driven multi-objective optimization of surfactant-modified nano-organic working fluids for enhanced heat transfer in ORC phase-change processes

IF 9.9 1区 工程技术 Q1 ENERGY & FUELS
Yong-qiang Feng , Zhi-xin Wang , Kang-jing Xu , Zhen-zhen Yang , Mu-ye Liu , Hua-jian Wu , Yong-zhen Wang , Zhi-xia He
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引用次数: 0

Abstract

Surfactant-mediated interfacial interactions in nano-organic working fluids effectively modulate colloidal thermophysical properties, leading to marked improvements in suspension stability and heat transfer performance within organic Rankine cycle (ORC) systems. Based on experimental data for nano-organic working fluids with three surfactants (CTAB, SDBS, and Span80), this study employs machine learning framework for prediction and optimization of the heat transfer and flow characteristics in ORC phase-change processes. A back propagation artificial neural network (BPANN) model is developed using 350 sets of transient steady-state experimental data, with the first 280 sets as training samples and the last 70 sets for testing, configured with 13 hidden nodes, a learning rate of 0.4, and the trainlm training function. The effects of six key operation parameters on evaporation and condensation heat transfer coefficients are investigated. A further bi-objective optimization considering maximum heat transfer coefficient and minimum pressure drop is addressed, while the Pareto-optimal solutions for surfactant-modified nano-organic working fluid is obtained. Results indicate that the evaporation heat transfer coefficient presents a parabolic trend with outlet dryness, while the condensation heat transfer coefficient increases with the mass flow rate. Increasing the evaporation heat transfer coefficient will deteriorate the condensation heat transfer coefficient. The optimal evaporation heat transfer coefficients and condensation heat transfer coefficients for 0.4 %SDBS + 0.1 %TiO2/R123, 0.3 %CTAB + 0.1 %TiO2/R123, and 0.3 %Span80 + 0.1 %TiO2/R123 are 3986.03 W/(m2·K) and 851.23 W/(m2·K), 4139.71 W/(m2·K) and 825.25 W/(m2·K), and 4180.3 W/(m2·K) and 724.32 W/(m2·K), which are 23.76 % higher and 38.01 % higher, 28.53 % higher and 33.79 % higher, 29.79 % higher and 17.44 % higher than that of 0.1 %TiO2/R123 of 3220.88 W/(m2·K) and 616.78 W/(m2·K), respectively. The nano-organic working fluid with Span80 demonstrates superior performance due to its relatively high comprehensive heat transfer coefficients for both evaporation and condensation.
数据驱动多目标优化表面活性剂改性纳米有机工作流体,增强 ORC 相变过程中的传热效果
纳米有机工作流体中表面活性剂介导的界面相互作用有效地调节了胶体的热物理性质,从而显著改善了有机朗肯循环(ORC)体系中的悬浮稳定性和传热性能。基于三种表面活性剂(CTAB、SDBS和Span80)的纳米有机工质实验数据,采用机器学习框架对ORC相变过程的传热和流动特性进行预测和优化。利用350组暂态稳态实验数据,以前280组作为训练样本,后70组作为测试样本,配置13个隐藏节点,学习率为0.4,训练函数为trainlm,建立了反向传播神经网络(BPANN)模型。研究了6个关键操作参数对蒸发和冷凝换热系数的影响。进一步研究了考虑最大传热系数和最小压降的双目标优化问题,得到了表面活性剂修饰纳米有机工质的pareto最优解。结果表明:蒸发换热系数随出口干燥度呈抛物线型变化,冷凝换热系数随质量流量增大而增大;增大蒸发换热系数会使冷凝换热系数变差。0.4% SDBS + 0.1% TiO2/R123、0.3% CTAB + 0.1% TiO2/R123、0.3% Span80 + 0.1% TiO2/R123的最佳蒸发换热系数和冷凝换热系数分别为3986.03 W/(m2·K)和851.23 W/(m2·K)、4139.71 W/(m2·K)和825.25 W/(m2·K)、4180.3 W/(m2·K)和724.32 W/(m2·K),分别比0.1% TiO2/R123的3220.88 W/(m2·K)和616.78 W/(m2·K)高23.76%和38.01%、28.53%和33.79%、29.79%和17.44%;分别。含Span80的纳米有机工质具有较高的蒸发和冷凝综合传热系数,表现出优异的性能。
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来源期刊
Energy Conversion and Management
Energy Conversion and Management 工程技术-力学
CiteScore
19.00
自引率
11.50%
发文量
1304
审稿时长
17 days
期刊介绍: The journal Energy Conversion and Management provides a forum for publishing original contributions and comprehensive technical review articles of interdisciplinary and original research on all important energy topics. The topics considered include energy generation, utilization, conversion, storage, transmission, conservation, management and sustainability. These topics typically involve various types of energy such as mechanical, thermal, nuclear, chemical, electromagnetic, magnetic and electric. These energy types cover all known energy resources, including renewable resources (e.g., solar, bio, hydro, wind, geothermal and ocean energy), fossil fuels and nuclear resources.
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