An advanced approach for cloud enabled energy efficient ventilation control of multiple main fans in underground coal mines

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computers & Electrical Engineering Pub Date : 2025-05-01 DOI:10.1016/j.compeleceng.2025.110330

Prasad Bhukya, Krishna Naick Bhukya

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引用次数: 0

Abstract

Underground mine ventilation ensures air quality and climate control, with the main fan consuming 50 % of total mine energy. This research recommends a hybrid deep learning model that combines the Advanced Multi-head Cross Attention-based Bidirectional long short-term memory Network (AMCABN) with Multi-strategy Enhanced Mantis Search Algorithm (MEMSA) to solve the time-consuming nature of existing systems. The objective is to optimize fan power consumption by integrating cloud data using a hybrid model. Two model predictions are generated using real-time sensor data sent to the cloud: one for the ideal fan power requirement and another for the amount of time required to lower the methane concentration. The proposed strategy is verified using a matrix laboratory environment and compared with others. The proposed method reduces fan power consumption to 70 kW, outperforming genetic and differential evolution algorithms, and converges faster within 100 iterations, demonstrating higher efficiency in optimizing fan performance.

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煤矿井下多主风机云化节能通风控制的先进方法

矿井地下通风保证了空气质量和气候控制，主风机能耗占矿井总能耗的50%。本研究提出了一种混合深度学习模型，该模型将基于高级多头交叉注意的双向长短期记忆网络（AMCABN）与多策略增强螳螂搜索算法（MEMSA）相结合，以解决现有系统的耗时特性。目标是通过使用混合模型集成云数据来优化风扇功耗。使用发送到云的实时传感器数据生成两个模型预测：一个是理想的风扇功率需求，另一个是降低甲烷浓度所需的时间。利用矩阵实验室环境验证了所提出的策略，并与其他策略进行了比较。该方法将风扇功耗降低至70 kW，优于遗传和差分进化算法，并且在100次迭代内收敛速度更快，对风扇性能的优化效率更高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers & Electrical Engineering 工程技术-工程：电子与电气

CiteScore

9.20

自引率

7.00%

发文量

661

审稿时长

47 days

期刊介绍： The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.