Prediction Model of Coal and Gas Outburst Based on Rough Set-Unascertained Measure Theory

This paper proposes a risk evaluation model based on rough sets (RS) and the unascertained measure theory (UMT) for solving the accuracy problem of coal and gas outburst prediction with the aim to reduce economic losses and casualties in coal mining. The coal and gas outburst prediction problem is constrained by the selection of the prediction indexes, the coupling of a single index, and the weight of each index. The proposed RS-UMT model applies two modified techniques. The first one is a method for index weight determination that was improved by rough set theory. The second one is a method for coupling a single index that was modified by the unascertained measure theory. The RS-UMT model not only well solves the problem of coupling a single index of coal and gas outbursts, but also solves the problem that the weight is susceptible to subjective factors and prior knowledge. The RS-UMT model was used to judge the risk degree of outburst of 10 mining faces in the Pingdingshan No. 8 Mine and No. 10 Mine. The predictive results of the model were basically identical to the actual measured results. The performance of the RS-UMT model was also compared to existing methods. Based on the case study it can be concluded that the RS-UMT model is an accurate and very promising method for solving the coal and gas outburst prediction problem

Advances in Unconventional Oil and Gas

This book focuses on the latest progress in unconventional oil and gas (such as coalbed methane, shale gas, tight gas, heavy oil, hydrate, etc.) exploration and development, including reservoir characterization, gas origin and storage, accumulation geology, hydrocarbon generation evolution, fracturing technology, enhanced oil recovery, etc. Some new methods are proposed to improve the gas extraction in coal seams, characterize the relative permeability of reservoirs, improve the heat control effect of hydrate-bearing sediment, improve the development efficiency of heavy oil, increase fracturing effectiveness in tight reservoirs, etc

Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction

Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the highfrequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction

Advanced perception and control method of harmful gas during construction period of coal tunnel based on DeepAR

Effective real-time treatment and control of harmful gases are key to ensuring the safety of tunnel construction workers. Currently, the monitoring ability of harmful gases is insufficient to match the processing needs, which poses significant risks to the safety of tunnel construction workers. This paper proposes an advanced perception and treatment method for harmful gases during tunnel construction, utilizing the DeepAR algorithm. Real-time monitoring of the concentration and diffusion of harmful gases is conducted, and a harmful gas concentration prediction model is established using the DeepAR algorithm, achieving advanced perception of harmful gases during tunnel construction. The harmful gas treatment plan is developed in advance, and the effectiveness of the proposed method is demonstrated by simulation testing under realistic field scenarios and comparing with other prediction models. The method was applied in a coal mine tunnel in Qinghai Province, achieving an accuracy rate of 94.3%, which is higher compared to those obtained using RNN and LSTM algorithms. Moreover, the computational time is less than 60 s. The method provides timely perception of the concentration distribution of harmful gases in the tunnel and proposes targeted treatment measures, verifying the effectiveness of the prediction model from the perspective of practical engineering application

A comprehensive system for detection of flammable and toxic gases using IoT

The majority of the existing gases constitute a risk to the health of humans and the environment in general. There is a wide range of diseases that can arise as a result of exposure to toxic and dangerous gases, which can be a cause of death or serious health problems. More so, deadly explosions may occur as a result of leakages of such gases. However, such consequences can be avoided when these dangerous gases are not detected early. Many researchers have proposed different kinds of systems for the detection of gas leakage, but most of the proposed systems were mainly designed to detect LPG gas. Therefore, in this study, a system is proposed for detecting different kinds of flammable and toxic gases. The gases that are detectable by the proposed system include smoke, Ethanol, CNG Gas, Methane, toluene, propane, Carbon Monoxide, acetone, Hydrogen Gas, and Formaldehyde. The system can detect gases efficiently and release evacuation alarms simultaneously, then send SMS for emergencies. The proposed system is ready to use and can be installed at any work location

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

Rock-burst occurrence prediction based on optimized naïve bayes models

Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors