Temperature Prediction Model in the Main Ventilation System of an Underground Mine

Bascompta, Marc; Rossell, Josep M.; Pera, Lluís Sanmiquel; Anticoi, Hernan

doi:10.3390/app10207238

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…In addition, the in situ observation and artificial acquisition of data are arduous. For example, Marc Bascompta [44] acquired the data of such variables as dry/wet temperature and airflow, Numerical simulation is an effective method for scholars to study the change of temperature and humidity in a roadway. Based on numerical simulation, Xu et al [30] and Li et al [31] explored the flow and heat transfer law of thermal fluid during geothermal mining in rock strata.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the in situ observation and artificial acquisition of data are arduous. For example, Marc Bascompta [44] acquired the data of such variables as dry/wet temperature and airflow, at seven control points continuously for 3 years, and this process consumed excessive time and funds. Fortunately, similarity simulation experiments can remarkably shorten the time.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Temperature and Humidity in Roadway with Water Trickling Using Principal Component Analysis-Long Short-Term Memory-Genetic Algorithm Method

Wu,

Jia,

Zhang

et al. 2023

Applied Sciences

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The heat dissipated from high geo-temperature underground surrounding rocks is the main heat source of working faces, while thermal water upwelling and trickling into the roadway will notably deteriorate the mine’s climate and thermal comfort. Predicting airflow temperature and relative humidity (RH) is conductive to intelligent control of air conditioning cooling and ventilation regulation. To accommodate this issue, an intelligent technique was proposed, integrating a genetic algorithm (GA) and long short-term memory (LSTM) based on rock temperature, inlet air temperature, water temperature, water flow rate, RH, and ventilation time. A total of 21 input features including over 200 pieces of data were collected from an independently developed modeling roadway to construct a dataset. Principal component analysis (PCA) was conducted to reduce features, and GA was used to tune the LSTM and PCA-LSTM architectures for best performance. The following research results were yielded. The proposed PCA-LSTM-GA model is more reliable and efficient than the single LSTM model or hybrid LSTM-GA model in predicting the air temperature Tfout and humidity RHout at the end of the water trickling roadway. The importance scores (ISs) indicate that Tfout is mainly influenced by the surrounding rock temperature (IS 0.661) and the inlet air temperature (IS 0.264). While RHout is primarily influenced by the rock temperature in the water trickling section (IS 0.577), the inlet air temperature (IS 0.187), and the trickling water temperature and flow rate (total IS 0.136), and it has an evident time effect. In addition, we developed relevant equipment and provided engineering practice methods to use the machine learning model. The proposed model, which can predict the mine microclimate, serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control.

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Section: Introductionmentioning

confidence: 99%

“…In addition, the in situ observation and artificial acquisition of data are arduous. For example, Marc Bascompta [44] acquired the data of such variables as dry/wet temperature and airflow, at seven control points continuously for 3 years, and this process consumed excessive time and funds. Fortunately, similarity simulation experiments can remarkably shorten the time.…”

Section: Introductionmentioning

confidence: 99%

Predicting Temperature and Humidity in Roadway with Water Trickling Using Principal Component Analysis-Long Short-Term Memory-Genetic Algorithm Method

Wu,

Jia,

Zhang

et al. 2023

Applied Sciences

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A thermal environment prediction method for a mine ventilation roadway based on a numerical method: A case study

Li²,

Jalilinasrabady

et al. 2023

Case Studies in Thermal Engineering

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The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility

Chimunhu,

Topal,

Asad

et al. 2024

Mining Technology: Transactions of the Institutions of Mining a

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Machine learning (ML) applications are increasing their footprint in underground mine planning, enabled by the gradual enrichment of research methods. Indeed, improvements in prediction results have been accelerated in areas such as mining dilution, stope stability, ore grade, and equipment availability, among others. In addition, the increasing deployment of equipment with digital technologies and rapid information retrieval sensor networks is resulting in the production of immense quantities of operational data. However, despite these favourable developments, optimisation studies on key input activities are still siloed, with minimal or no synergies towards the primary objective of optimising the production schedule. As such, the full potential of ML benefits is not realised. To explore the potential benefits, this study outlines primary input areas in production scheduling for reference and limits the scope to six key areas, covering dilution prediction, ore grade variability, geotechnical stability, ventilation, mineral commodity prices and data management. The study then delves into the literature of each before examining the limitations of existing common applications, including ML. Finally, conclusions with recommendations/solutions to enhance resilience, global optimality, and reliability of the production schedule through synergistic nexus with function-specific optimised input models are presented.

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Temperature Prediction Model in the Main Ventilation System of an Underground Mine

Cited by 4 publications

References 37 publications

Predicting Temperature and Humidity in Roadway with Water Trickling Using Principal Component Analysis-Long Short-Term Memory-Genetic Algorithm Method

Predicting Temperature and Humidity in Roadway with Water Trickling Using Principal Component Analysis-Long Short-Term Memory-Genetic Algorithm Method

A thermal environment prediction method for a mine ventilation roadway based on a numerical method: A case study

The future of underground mine planning in the era of machine learning: Opportunities for engineering robustness and flexibility

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