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

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