Mine safety and risk prediction mechanism through nanocomposite and heuristic optimization algorithm

Abstract

Worker's safety and water quality maintenance are the two predominant process that is followed inside the Mines. Many models had been proposed in the past to enhance the whole process but they failed to do so due to the practical limitations in the deployment in terms of cost. Though technology had improved much, human intervention is still unavoidable in mines. Considering the practical issues, in this article, a novel methodology using the Internet of Things (IoT) is proposed. This study is mainly focused on the change in the geometry/structure of the coal mining field due to pressure and water contamination that occurs in the region during coal extraction. Several sensors are adopted to measure the water quality and computational technique is used to analyze roof pressure variation in the coalfield. The roof pressure experienced by the nascent structure is compared with a structure that is roof bonded with a glass fiber composite layer. The significance of nanofiber technology is analyzed and reported. To ensure water quality, Harmonic Water Optimization (HWO) Algorithm is developed, here 10 different water flow areas are chosen and their corresponding data is gathered using IoT sensors. Harmony Management Preprocessing (HMP) Algorithm is developed to preprocess the redundant and ambiguous data. Heuristic Fusion Clustering (HFC)algorithm is developed for the data fusion, which can cluster and improve the data quality. The analysis of the proposed work reveals that it would greatly improve the sustainable development of mining safety and water quality which is the most vital factor.

Keywords

• Coal mining
• Pressure
• Water contamination
• Data filter
• Data fusion
• Mining safety