UMAP and LSTM based fire status and explosibility prediction for sealed-off area in underground coal mine

Kumari, Khushbu; Dey, Prasanjit; Kumar, Chandan; Pandit, Dewangshu; Mishra, Subhashree; Kisku, Vikash; Chaulya, S.K.; Ray, S. K.; Prasad, G.M.

doi:10.1016/j.psep.2020.12.019

Cited by 40 publications

(8 citation statements)

References 18 publications

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“…The combined model was compiled with the Adam optimizer[26-28] at a learning rate of 0.0001 and categorical cross-entropy loss function [29,30], optimizing for multi-class classi cation performance: (7) where y i denotes the true label, denotes the predicted label for each sample in the batch of size N.…”

Section: Prediction Model Buildingmentioning

confidence: 99%

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Gbadago,

Hwang,

Lee

et al. 2024

Preprint

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The increasing global demand for eco-friendly products is driving innovation in sustainable chemical synthesis, particularly the development of biodegradable substances. Herein, a novel method utilizing artificial intelligence (AI) to predict the biodegradability of organic compounds is presented, overcoming the limitations of traditional prediction methods that rely on laborious and costly density functional theory (DFT) calculations. We propose leveraging readily available molecular formulas and structures represented by simplified molecular-input line-entry system (SMILES) notation and molecular images to develop an effective AI-based prediction model using state-of-the-art machine learning techniques, including deep convolutional neural networks (CNN) and long-short term memory (LSTM) learning algorithms, capable of extracting meaningful molecular features and spatiotemporal relationships. The model is further enhanced with reinforcement learning (RL) to better predict and discover new biodegradable materials by rewarding the system for identifying unique and biodegradable compounds. The combined CNN-LSTM model achieved an 87.2% prediction accuracy, outperforming CNN- (75.4%) and LSTM-only (79.3%) models. The RL-assisted generator model produced approximately 60% valid SMILES structures, with over 80% being unique to the training dataset, demonstrating the model's capability to generate novel compounds with potential for practical application in sustainable chemistry. The model was extended to develop novel electrolytes with desired molecular weight distribution.

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Section: Prediction Model Buildingmentioning

confidence: 99%

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Gbadago,

Hwang,

Lee

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, LSTM has a good ability to detect patterns in time series, such as trends, autocorrelations, seasonality, and noise. It succeeded to predict different engineering time series such as explosibility of underground mines [ 24 ], electricity load [ 25 ], soil behavior [ 26 ], water yield of solar stills [ 27 ] and air pollution [ 28 ].…”

Section: Artificial Intelligence Modelsmentioning

confidence: 99%

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Elsheikh

Saba

Panchal³

et al. 2021

Healthcare

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Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

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“…Deng et al , studied the dangerous range of coal spontaneous combustion under the condition of gas drainage and determined the relationship between the pipeline position of gas drainage and the oxidation heating zone. Using the machine learning of UMAP and LSTM models, Kumari predicted the tendency of the mixed gas concentration, as well as the fire state, in a mine fire district. Chu et al studied the disturbance effect and disaster mechanism of coal spontaneous combustion by using the gas drainage technology for pressure relief.…”

Section: Introductionmentioning

confidence: 99%

Optimal Parameters of Gas Drainage and Carbon Dioxide Inerting Technology and Its Application in a High Gassy and Spontaneous Combustion Mine

Chun-cheng

et al. 2022

ACS Omega

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In the process of coupling disaster prevention and control of gas and coal spontaneous combustion in goaf, there is a great contradiction between the gas drainage and carbon dioxide inerting technology. The key performance indexes are put forward to solve the coupling disaster, such as the air quantity of the intake airway (A), the gas drainage rate (B), the carbon dioxide injection rate (C), and the injection depth (D). Using the numerical simulation method and the orthogonal test of four factors and three levels, we establish the coupling disaster model of the no. 7436 working face in the Kongzhuang coal mine. Using a combination of the relative membership degree method and range analysis, the optimal level of each factor is determined, which is A II B III C II D II . Furthermore, the distribution law of the airflow field is obtained under the conditions of different gas drainage rates and carbon dioxide injection rates. The results show that the gas concentration decreases with an increase in gas drainage in the upper corner, but it has little impact on the width of the oxidation zone. The gas concentration can be reduced to 1%, while the gas drainage rate is higher than 35 m 3 /min. With an increase in gas injection rate, the carbon dioxide emission rate increases in the upper corner, but the width of the oxidation zone decreases. Also, the gas injection rate should be less than 800 m 3 /h. Moreover, with an increase in injection time in the upper corner during the injection process, the carbon dioxide and gas concentrations increase, and the maximum carbon dioxide concentration is 1.3%, and the maximum gas concentration is 0.42%, which is consistent with the results of numerical simulations.

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UMAP and LSTM based fire status and explosibility prediction for sealed-off area in underground coal mine

Cited by 40 publications

References 18 publications

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Optimal Parameters of Gas Drainage and Carbon Dioxide Inerting Technology and Its Application in a High Gassy and Spontaneous Combustion Mine

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