基于GA-BP神经网络模型预测水基炭黑-胶原蛋白纳米流体热导率和黏度

doi:10.16085/j.issn.1000-6613.2023-0961

摘要/Abstract

摘要：

纳米流体由于其独特的强化传热性能，已广泛应用于各个领域。而热导率和黏度直接影响纳米流体在实际工程中的适用性，因此在考察纳米流体的强化传热特性前首先要分析研究其热导率和黏度。本研究利用炭黑和胶原蛋白，采用两步法制备了水基炭黑胶原蛋白纳米流体。实验分析了炭黑和胶原蛋白质量分数、温度对纳米流体热导率和黏度的影响。采用灰色关联方法对这些参数的权重进行了数学计算，基于实验数据建立了三输入两输出的BP神经网络预测模型，并利用遗传算法（GA）对BP模型进行优化。结果表明，遗传算法优化后的BP神经网络模型对预测输出具有更高的准确性和更好的稳定性，回归系数和最大偏差分别为0.99918和0.002。本研究不仅对于理解和控制水基炭黑-胶原蛋白纳米流体的热物理性能有重要意义，而且为工程设计和材料科学等方面的应用提供了新思路。

关键词: 纳米流体, 炭黑, 胶原蛋白, BP神经网络, 热导率, 黏度

Abstract:

Nanofluids have been widely used in various fields due to their unique enhanced heat transfer properties. The thermal conductivity and viscosity directly affect the applicability of nanofluids in practical engineering, so before examining the enhanced heat transfer characteristics of nanofluids, it is first necessary to analyze and study their thermal conductivity and viscosity. In this study, water-based carbon black collagen nanofluids were prepared by a two-step method using carbon black and collagen. The effects of carbon black and collagen concentration and temperature on the thermal conductivity and viscosity of nanofluids were analyzed. The weights of these parameters were mathematically calculated by the gray correlation method, and a BP neural network prediction model with three inputs and two outputs was established based on the experimental data, and the BP model was optimized by genetic algorithm (GA). The results showed that the BP neural network model optimized by the genetic algorithm had higher accuracy and better stability for the predicted output, and the regression coefficient and maximum deviation were 0.99918 and 0.002, respectively. This study was not only of great significance for understanding and controlling the thermophysical properties of water-based carbon black-collagen nanofluids, but also provided new ideas for the application of engineering design and materials science.

Key words: nanofluids, carbon black, collagen, BP neural networks, thermal conductivity, viscosity

中图分类号:

TK01⁺9

李凯, 魏鹤琳, 尹志凡, 左夏华, 于晓宇, 尹宏远, 杨卫民, 阎华, 安瑛. 基于GA-BP神经网络模型预测水基炭黑-胶原蛋白纳米流体热导率和黏度[J]. 化工进展, 2024, 43(7): 4138-4147.

LI Kai, WEI Helin, YIN Zhifan, ZUO Xiahua, YU Xiaoyu, YIN Hongyuan, YANG Weimin, YAN Hua, AN Ying. Prediction of thermal conductivity and viscosity of water-based carbon black nanofluids based on GA-BP neural network model[J]. Chemical Industry and Engineering Progress, 2024, 43(7): 4138-4147.

图/表 17

图1 纳米流体制备

图2 热导率仪

图3 旋转黏度计

图4 纳米流体中炭黑粒子的TEM图像

图5 水基炭黑纳米流体的EDS能谱

图6 不同炭黑质量分数和温度下纳米流体的热导率

图7 不同胶原蛋白质量分数和温度下纳米流体的热导率

图8 不同炭黑质量分数和温度下纳米流体的黏度

图9 不同胶原蛋白质量分数和温度下纳米流体的黏度

表1 灰色关联度分析

影响因素	热导率	黏度
炭黑质量分数	0.852	0.746
胶原蛋白质量分数	0.667	0.856
温度	0.808	0.538

图10 BP神经网络结构

图11 不同隐含层节点的R2和MSE

图12 GA-BP神经网络流程图

图13 遗传算法个体适应度优化曲线

图14 神经网络预测热导率回归曲线

图15 预测结果与实际值之间的比较

图16 预测结果偏差之间的比较

参考文献 41

1	BAHIRAEI Mehdi, RAHMANI Reza, YAGHOOBI Ali, et al. Recent research contributions concerning use of nanofluids in heat exchangers: A critical review[J]. Applied Thermal Engineering, 2018, 133: 137-159.
2	YAZDANIFARD Farideh, AMERI Mehran, Ehsan EBRAHIMNIA-BAJESTAN. Performance of nanofluid-based photovoltaic/thermal systems: A review[J]. Renewable and Sustainable Energy Reviews, 2017, 76: 323-352.
3	SAJID Muhammad Usman, Hafiz Muhammad ALI. Recent advances in application of nanofluids in heat transfer devices: A critical review[J]. Renewable and Sustainable Energy Reviews, 2019, 103: 556-592.
4	SAHIN Ahmet Z, UDDIN Mohammed Ayaz, YILBAS Bekir S, et al. Performance enhancement of solar energy systems using nanofluids: An updated review[J]. Renewable Energy, 2020, 145: 1126-1148.
5	CUCE Erdem, CUCE Pinar Mert, GUCLU Tamer, et al. On the use of nanofluids in solar energy applications[J]. Journal of Thermal Science, 2020, 29(3): 513-534.
6	MADHESH D, PARAMESHWARAN R, KALAISELVAM S. Experimental investigation on convective heat transfer and rheological characteristics of Cu-TiO₂ hybrid nanofluids[J]. Experimental Thermal and Fluid Science, 2014, 52: 104-115.
7	YANG Liu, XU Jianyong, DU Kai, et al. Recent developments on viscosity and thermal conductivity of nanofluids[J]. Powder Technology, 2017, 317: 348-369.
8	CHIAM H W, AZMI W H, USRI N A, et al. Thermal conductivity and viscosity of Al₂O₃ nanofluids for different based ratio of water and ethylene glycol mixture[J]. Experimental Thermal and Fluid Science, 2017, 81: 420-429.
9	SYAM SUNDAR L, VENKATA RAMANA E, SINGH Manoj K, et al. Thermal conductivity and viscosity of stabilized ethylene glycol and water mixture Al₂O₃ nanofluids for heat transfer applications: An experimental study[J]. International Communications in Heat and Mass Transfer, 2014, 56: 86-95.
10	SYAM SUNDAR L, SINGH Manoj K, SOUSA Antonio C M. Investigation of thermal conductivity and viscosity of Fe₃O₄ nanofluid for heat transfer applications[J]. International Communications in Heat and Mass Transfer, 2013, 44: 7-14.
11	ALAWI Omer A, SIDIK Nor Azwadi Che, XIAN Hongwei, et al. Thermal conductivity and viscosity models of metallic oxides nanofluids[J]. International Journal of Heat and Mass Transfer, 2018, 116: 1314-1325.
12	LEE Gyoung-Ja, KIM Chang Kyu, LEE Min ku, et al. Thermal conductivity enhancement of ZnO nanofluid using a one-step physical method[J]. Thermochimica Acta, 2012, 542: 24-27.
13	ESFE Hemmat Mohammad, AFRAND Masoud, GHAREHKHANI Samira, et al. An experimental study on viscosity of alumina-engine oil: Effects of temperature and nanoparticles concentration[J]. International Communications in Heat and Mass Transfer, 2016, 76: 202-208.
14	BARATPOUR Mohsen, KARIMIPOUR Arash, AFRAND Masoud, et al. Effects of temperature and concentration on the viscosity of nanofluids made of single-wall carbon nanotubes in ethylene glycol[J]. International Communications in Heat and Mass Transfer, 2016, 74: 108-113.
15	WUSIMAN Kuerbanjiang, JEONG Hyomin, TULUGAN Kelimu, et al. Thermal performance of multi-walled carbon nanotubes (MWCNTs) in aqueous suspensions with surfactants SDBS and SDS[J]. International Communications in Heat and Mass Transfer, 2013, 41: 28-33.
16	KIM Nam-Jin, PARK Sung-Seek, Sang Hoon LIM, et al. A study on the characteristics of carbon nanofluids at the room temperature (25℃)[J]. International Communications in Heat and Mass Transfer, 2011, 38(3): 313-318.
17	XU J Z, GAO B Z, KANG F Y. A reconstruction of Maxwell model for effective thermal conductivity of composite materials[J]. Applied Thermal Engineering, 2016, 102: 972-979.
18	CHEN Haisheng, DING Yulong, HE Yurong, et al. Rheological behaviour of ethylene glycol based titania nanofluids[J]. Chemical Physics Letters, 2007, 444(4/5/6): 333-337.
19	AFRAND Masoud, TOGHRAIE Davood, SINA Nima. Experimental study on thermal conductivity of water-based Fe₃O₄ nanofluid: Development of a new correlation and modeled by artificial neural network[J]. International Communications in Heat and Mass Transfer, 2016, 75: 262-269.
20	KHDHER Abdolbaqi Mohammed, SIDIK Nor Azwadi Che, HAMZAH Wan Azmi Wan, et al. An experimental determination of thermal conductivity and electrical conductivity of bio glycol based Al₂O₃ nanofluids and development of new correlation[J]. International Communications in Heat and Mass Transfer, 2016, 73: 75-83.
21	VILLARRUBIA Gabriel, DE PAZ Juan F, CHAMOSO Pablo, et al. Artificial neural networks used in optimization problems[J]. Neurocomputing, 2018, 272: 10-16.
22	Seongmin HEO, LEE Jay H. Fault detection and classification using artificial neural networks[J]. IFAC-PapersOnLine, 2018, 51(18): 470-475.
23	CHRIS TSENG H, ALMOGAHED Bassam. Modular neural networks with applications to pattern profiling problems[J]. Neurocomputing, 2009, 72(10/11/12): 2093-2100.
24	David Daniel COX, DEAN Thomas. Neural networks and neuroscience-inspired computer vision[J]. Current Biology, 2014, 24(18): R921-R929.
25	ADUN Humphrey, Ifeoluwa WOLE-OSHO, OKONKWO Eric C, et al. A neural network-based predictive model for the thermal conductivity of hybrid nanofluids[J]. International Communications in Heat and Mass Transfer, 2020, 119: 104930.
26	QING Haohua, HAMEDI Sajad, EFTEKHARI S ALI, et al. A well-trained feed-forward perceptron Artificial Neural Network (ANN) for prediction the dynamic viscosity of Al₂O₃-MWCNT ( 40 ∶ 60 ) -Oil SAE50 hybrid nano-lubricant at different volume fraction of nanoparticles, temperatures, and shear rates[J]. International Communications in Heat and Mass Transfer, 2021, 128: 105624.
27	ESFE Hemmat Mohammad, MOTAHARI Kazem, SANATIZADEH Ehsan, et al. Estimation of thermal conductivity of CNTs-water in low temperature by artificial neural network and correlation[J]. International Communications in Heat and Mass Transfer, 2016, 76: 376-381.
28	ESFE Hemmat Mohammad, ROSTAMIAN Hadi, AFRAND Masoud, et al. Modeling and estimation of thermal conductivity of MgO-water/EG ( 60 ∶ 40 ) by artificial neural network and correlation[J]. International Communications in Heat and Mass Transfer, 2015, 68: 98-103.
29	ESFE Hemmat Mohammad, HASSANI AHANGAR Mohammad Reza, REJVANI Mousa, et al. Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO₂ using experimental data[J]. International Communications in Heat and Mass Transfer, 2016, 75: 192-196.
30	ESFE Hemmat Mohammd, WONGWISES Somchai, NADERI Ali, et al. Thermal conductivity of Cu/TiO₂-water/EG hybrid nanofluid: Experimental data and modeling using artificial neural network and correlation[J]. International Communications in Heat and Mass Transfer, 2015, 66: 100-104.
31	ESFE Hemmat Mohammad, SAEDODIN Seyfolah, NADERI Ali, et al. Modeling of thermal conductivity of ZnO-EG using experimental data and ANN methods[J]. International Communications in Heat and Mass Transfer, 2015, 63: 35-40.
32	ESFE Hemmat Mohammad, AFRAND Masoud, YAN Wei-Mon, et al. Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al₂O₃-water nanofluids using experimental data[J]. International Communications in Heat and Mass Transfer, 2015, 66: 246-249.
33	ESFE Hemmat Mohammad, BAHIRAEI Mehdi, HAJMOHAMMAD Mohammad Hadi, et al. Rheological characteristics of MgO/oil nanolubricants: Experimental study and neural network modeling[J]. International Communications in Heat and Mass Transfer, 2017, 86: 245-252.
34	ESFE Hemmat Mohammad, SAEDODIN Seyfolah, SINA Nima, et al. Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid[J]. International Communications in Heat and Mass Transfer, 2015, 68: 50-57.
35	ZHANG Huiying, YAN Suying, GAO Hongjian, et al. Experimental investigation and prediction of changes in thermal conductivity of carbon nanotube nanofluid[J]. International Communications in Heat and Mass Transfer, 2021, 127: 105526.
36	李凯, 魏鹤琳, 左夏华, 等. 水基炭黑-胶原蛋白纳米流体制备及稳定性实验[J]. 化工进展, 2024, 43(4): 1944-1952.
	LI Kai, WEI Helin, ZUO Xiahua, et al. Experimental study on the preparation and stability of water-based carbon black-collagen nanofluids[J]. Chemical Industry and Engineering Progress, 2024, 43(4): 1944-1952.
37	李浩然. ZnO纳米流体传热特性实验研究[D]. 哈尔滨: 哈尔滨工业大学, 2015.
	LI Haoran. Experimental investigation on heat transfer characteristics of ZnO nanofluids[D]. Harbin: Harbin Institute of Technology, 2015.
38	LI Xiaoke, ZOU Changjun, CHEN Wenjing, et al. Experimental investigation of β-cyclodextrin modified carbon nanotubes nanofluids for solar energy systems: Stability, optical properties and thermal conductivity[J]. Solar Energy Materials and Solar Cells, 2016, 157: 572-579.
39	WEN Jin, LI Xiaoke, CHEN Wenjing, et al. Systematical investigation on the solar-thermal conversion performance of TiN plasmonic nanofluids for the direct absorption solar collectors[J]. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2021, 624: 126837.
40	杨丰源. SX区块优势通道识别与分布特征研究[D]. 大庆: 东北石油大学, 2017.
	YANG Fengyuan. Research on the identifying and distribution characteristics the thief zones in SX area[D]. Daqing: Northeast Petroleum University, 2017.
41	刘璐, 杨丹, 陈睿杰, 等. 基于KPCA-GA-BP神经网络的POI质量预测研究[J]. 电信科学, 2023, 39(1): 108-116.
	LIU Lu, YANG Dan, CHEN Ruijie, et al. Research on POI quality prediction based on KPCA-GA-BP neural network[J]. Telecommunications Science, 2023, 39(1): 108-116.

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