Operation Optimization of Thermal Management System of Deep Metal Mine Based on Heat Current Method and Prediction Model

Wenpu Wang, Wei Shao (), Shuo Wang, Junling Liu, Kun Shao, Zhuoqun Cao, Yu Liu and Zheng Cui ()
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Wenpu Wang: Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China
Wei Shao: Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China
Shuo Wang: Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China
Junling Liu: Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China
Kun Shao: Shandong Institute of Advanced Technology, Jinan 250100, China
Zhuoqun Cao: Shandong Institute of Advanced Technology, Jinan 250100, China
Yu Liu: Institute of Thermal Science and Technology, Shandong University, Jinan 250061, China
Zheng Cui: Shandong Institute of Advanced Technology, Jinan 250100, China

Energies, 2023, vol. 16, issue 18, 1-21

Abstract: With the increasing depth of metal mining, thermal damage has become a serious problem that restricts mining. The thermal management system of refrigeration and ventilation is an indispensable technology in the mining of deep metal mines, which plays a key role in improving the thermal and humid environment of mines. Optimizing the performance of refrigeration and ventilation systems to reduce energy consumption has become a focus of researchers’ attention. Based on the heat current method, this research establishes the overall heat transfer and flow constraint model of the refrigeration and ventilation system, and proposes an iterative algorithm that combines the refrigerator energy consumption model and the artificial neural network model of heat exchangers. The Lagrange multiplier method is used to optimize the system with the goal of minimizing the total power consumption of the system. The results show that under 9.1 kW cooling load conditions, the total energy consumption of the system reduces by 16.5%, and the COP of the refrigerator increases by 11.6%. The optimization results provide significant guidance for the production and energy consumption reduction of the deep metal mines.

Keywords: heat current method; deep mine; operation optimization; Lagrange multiplier method; artificial neural network model (ANN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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