基于可解释机器学习的固体填充床储热装置快速设计研究

范昌浩; 李明佳; 李梦杰; 张腾; 张传琪

doi:10.7652/xjtuxb202411008

您当前的位置：

首页 >

文章列表页 >

基于可解释机器学习的固体填充床储热装置快速设计研究

—————————————————————— | 更新时间：2025-07-25

- 基于可解释机器学习的固体填充床储热装置快速设计研究
- Research on Rapid Design of Solid Packed-Bed Heat Storage Devices Based on Interpretable Machine Learning
- 西安交通大学学报 2024年58卷第11期页码：87-97
- 作者机构：
  
  1. 西安交通大学国家储能技术产教融合创新平台(中心),西安,710049
  2. 北京理工大学机械与车辆学院,北京,100081
- 作者简介：
  
  : 2024-02-09。作者简介: 范昌浩(1999—),男,博士生
  李明佳(通信作者),女,教授,博士生导师。基金项目: 国家自然科学基金资助项目(52293413,52076161)。
- 基金信息：
  
  : 2024-02-09。作者简介: 范昌浩(1999—),男,博士生;李明佳(通信作者),女,教授,博士生导师。基金项目: 国家自然科学基金资助项目(52293413,52076161)。
- DOI：10.7652/xjtuxb202411008
  中图分类号： TK124
- 网络首发：2024-11-10，
  
  纸质出版：2024
- 稿件说明：
移动端阅览
范昌浩, 李明佳, 李梦杰, 等. 基于可解释机器学习的固体填充床储热装置快速设计研究[J]. 西安交通大学学报, 2024,58(11):87-97.

FAN Changhao, LI Mingjia, LI Mengjie, et al. Research on Rapid Design of Solid Packed-Bed Heat Storage Devices Based on Interpretable Machine Learning[J]. 2024, 58(11): 87-97.
范昌浩, 李明佳, 李梦杰, 等. 基于可解释机器学习的固体填充床储热装置快速设计研究[J]. 西安交通大学学报, 2024,58(11):87-97. DOI： 10.7652/xjtuxb202411008.

FAN Changhao, LI Mingjia, LI Mengjie, et al. Research on Rapid Design of Solid Packed-Bed Heat Storage Devices Based on Interpretable Machine Learning[J]. 2024, 58(11): 87-97. DOI： 10.7652/xjtuxb202411008.

摘要

为了解决填充床储热装置性能计算耗时、设计方法匮乏、难以满足多种储热场景需求的问题

提出了一种具有一定通用性、能够快速给出准确设计结果的固体填充床储热装置设计方法。首先

建立了固体填充床储热装置储热效能数据集

基于此训练了可准确预测装置储热效能的人工神经网络模型; 其次

采用SHapley Additive exPlanation方法对人工神经网络模型的预测进行解释

量化了材料物性、装置结构尺寸以及运行参数对装置储热效能的影响; 最后

建立了固体填充床储热装置储热效能关联式

基于储热效能关联式提出了适用于多种储热场景的填充床储热装置设计流程

并以西班牙Andasol 1光热电站中的储热装置为实例进行了计算分析。研究结果表明:储热效能关联式计算结果与数值模拟结果的相对偏差在10%以内

可用于填充床储热装置储热效能的快速计算和准确预测; 与传统数值模拟方法相比

所提方法的计算效率提高了5个数量级

设计方案经济上可行

证明了该储热效能关联式在工程实践中的便利性和实用性。

Abstract

In order to address the issues of time-consuming performance calculations

lack of design methods

and difficulty in meeting various heat storage requirements for packed-bed heat storage devices

a design method for solid packed-bed heat storage devices is proposed which is versatile and can rapidly provide accurate design results. Firstly

a dataset of heat storage efficiency for solid packed-bed heat storage devices is established

and an artificial neural network model is trained to accurately predict the device's heat storage efficiency. Secondly

the SHapley Additive exPlanation method is used to explain the predictions of the artificial neural network model

quantifying the effects of material properties

device structure dimensions and operating parameters on the device's heat storage efficiency. Finally

a correlation formula for the heat storage efficiency of solid packed-bed heat storage devices is established. Based on the heat storage efficiency correlation formula

a design process for packed-bed heat storage devices suitable for various heat storage scenarios is proposed and analyzed using the heat storage device in the Andasol 1 solar thermal power station in Spain as a case study. The results demonstrate that the relative deviation of the heat storage efficiency correlation calculation results from the numerical simulation results is within 10%

suitable for rapid calculation and accurate prediction of heat storage efficiency for packed-bed heat storage devices. Compared to traditional numerical simulation methods

the computational efficiency is increased by five orders of magnitude. In addition

the design solution is economically feasible

emphasizing the convenience and practicality of the thermal storage efficiency correlation formula in engineering practice.

关键词

Keywords

references

HAN Xue, XU Chao, PAN Xinyu, et al. Dynamic analysis of a concentrating photovoltaic/concentrating solar power(CPV/CSP)hybrid system [J]. Science China Technological Sciences, 2019, 62(11): 1987-1998.

PIETRUSZKIEWICZ J, BRANDON B, HOLLENBACH R, et al. Solar thermocline storage systems: preliminary design study: NREL/TP-5500-48584[R]. Palo Alto: Electric Power Research Institute, 2010.

DE GRACIA A, CABEZA L F. Numerical simulation of a PCM packed bed system: a review [J]. Renewable and Sustainable Energy Reviews, 2017, 69: 1055-1063.

ORÓ E, CHIU J, MARTIN V, et al. Comparative study of different numerical models of packed bed thermal energy storage systems [J]. Applied Thermal Engineering, 2013, 50(1): 384-392.

WU Ming, LI Mingjia, XU Chao, et al. The impact of concrete structure on the thermal performance of the dual-media thermocline thermal storage tank using concrete as the solid medium [J]. Applied Energy, 2014, 113: 1363-1371.

LI Mingjia, JIN Bo, YAN Junjie, et al. Numerical and experimental study on the performance of a new two-layered high-temperature packed-bed thermal energy storage system with changed-diameter macro-encapsulation capsule [J]. Applied Thermal Engineering, 2018, 142: 830-845.

XU Chao, WANG Zhifeng, HE Yaling, et al. Sensitivity analysis of the numerical study on the thermal performance of a packed-bed molten salt thermocline thermal storage system [J]. Applied Energy, 2012, 92: 65-75.

LI Mengjie, QIU Yu, LI Mingjia. Cyclic thermal performance analysis of a traditional single-layered and of a novel multi-layered packed-bed molten salt thermocline tank [J]. Renewable Energy, 2018, 118: 565-578.

LI Peiwen, VAN LEW J, KARAKI W, et al. Generalized charts of energy storage effectiveness for thermocline heat storage tank design and calibration [J]. Solar Energy, 2011, 85(9): 2130-2143.

MA Zhao, LI Mingjia, YANG Weiwei, et al. General performance evaluation charts and effectiveness correlations for the design of thermocline heat storage system [J]. Chemical Engineering Science, 2018, 185: 105-115.

ANAND P, TEJES P K S, NAIK B K, et al. Design analysis and performance prediction of packed bed latent heat storage system employing machine learning models [J]. Journal of Energy Storage, 2023, 72(Part D): 108690.

YANG Xueming, CUI Jie, LI Yi, et al. Multi-factor analysis and optimization design of a cascaded packed-bed thermal storage system coupled with adiabatic compressed air energy storage [J]. Energy Conversion and Management, 2024, 300: 117961.

ZHANG Yan, TEOH B K, WU Maozhi, et al. Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence [J]. Energy, 2023, 262(Part A): 125468.

KIM S, ABBAS A, PYO J, et al. Developing a data-driven modeling framework for simulating a chemical accident in freshwater [J]. Journal of Cleaner Production, 2023, 425: 138842.

LEI Yang, CHEN Yuming, CHEN Jinghai, et al. A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas [J]. Energy, 2023, 273: 127126.

QIAO Qingyao, ESKANDARI H, SAADATMAND H, et al. An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector [J]. Energy, 2024, 286: 129499.

BIAEK J, BUJALSKI W, WOJDAN K, et al. Dataset level explanation of heat demand forecasting ANN with SHAP [J]. Energy, 2022, 261(Part A): 125075.

HÄNCHEN M, BRÜCKNER S, STEINFELD A. High-temperature thermal storage using a packed bed of rocks-heat transfer analysis and experimental validation [J]. Applied Thermal Engineering, 2011, 31(10): 1798-1806.

LI Mingjia, JIN Bo, MA Zhao, et al. Experimental and numerical study on the performance of a new high-temperature packed-bed thermal energy storage system with macroencapsulation of molten salt phase change material [J]. Applied Energy, 2018, 221: 1-15.

PACHECO J E, SHOWALTER S K, KOLB W J. Development of a molten-salt thermocline thermal storage system for parabolic trough plants [J]. Journal of Solar Energy Engineering, 2002, 124(2): 153-159.

HORNIK K, STINCHCOMBE M, WHITE H. Multilayer feedforward networks are universal approximators [J]. Neural Networks, 1989, 2(5): 359-366.

RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors [J]. Nature, 1986, 323(6088): 533-536.

周志华. 机器学习 [M]. 北京: 清华大学出版社, 2016.

LUNDBERG S M, LEE S I. A unified approach to interpreting model predictions [C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc., 2017: 4768-4777.

LOH W L. On Latin hypercube sampling [J]. The Annals of Statistics, 1996, 24(5): 2058-2080.

BAYÓN R, ROJAS E. Simulation of thermocline storage for solar thermal power plants: from dimensionless results to prototypes and real-size tanks [J]. International Journal of Heat and Mass Transfer, 2013, 60: 713-721.

ALMENDROS-IBÁÑEZ J A, FERNÁNDEZ-TORRIJOS M, DÍAZ-HERAS M, et al. A review of solar thermal energy storage in beds of particles: packed and fluidized beds [J]. Solar Energy, 2019, 192: 193-237.

STRASSER M N, SELVAM R P. A cost and performance comparison of packed bed and structured thermocline thermal energy storage systems [J]. Solar Energy, 2014, 108: 390-402.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

超临界二氧化碳向心透平轮背间隙内风阻损失及流动特性研究

张角凹坑气膜孔对双层壁气膜冷却特性的影响

面向高海拔空冷发电机防晕性能演化规律的电热耦合研究

冷库水平空气幕热湿阻隔性能数值模拟研究

水蒸气掺混对氨裂解气旋流燃烧与排放特性的影响研究