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Advancements in machine learning techniques for coal and gas outburst prediction in underground mines

INTERNATIONAL JOURNAL OF COAL GEOLOGY 2024年 285卷

作者： Anani, Angelina Adewuyi, Sefiu O. Risso, Nathalie Nyaaba, Wedam Univ Arizona Dept Min & Geol Engn 1235 James E Rogers Way Tucson AZ 85719 USA Univ Illinois Dept Orthopaed 835 S Wolcott Ave Chicago IL 60612 USA

Coal and gas outbursts are a major cause of fatalities in underground coal mines and pose a threat to coal power generation worldwide. Among the current mitigation efforts include monitoring methane gas levels using sen-sors, employing geophysical surveys to identify geological structures and zones prone to outbursts, and using empirical modeling for outburst predictions. However, in the wake of industry 4.0 technologies, several studies have been conducted on applying artificial intelligence methods to predict outbursts. The proposed models and their results vary significantly in the literature. This study reviews the application of machine learning (ML) to predict coal and gas outbursts in underground mines using a mixed-method approach. Most of the available literature, with a focus on ML applications in coal and gas outburst prediction, was investigated in China. Findings indicate that researchers proposed diverse ML models mostly combined with different optimization algorithms, including particle swarm optimization (PSO), genetic algorithm (GA), rough set (RS), and fruit fly optimization algorithm (IFOA) for outburst prediction. The number and type of input parameters used for prediction differed significantly, with initial gas velocity being the most dominant parameter for gas outbursts, and coal seam depth as the dominant parameter for coal outbursts. The datasets for training and testing the proposed ML models in the literature varied significantly but were mostly insufficient - which questions the reliability of some of the applied ML models. Future research should investigate the effect of data size and input parameters on coal and gas outburst prediction.

关键词： Mining hazard Machine learning optimization algorithms Artificial intelligence Coal mining

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Universal algorithm for the analysis of charge distributions in segmented electrodes of gas detectors

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EUROPEAN PHYSICAL JOURNAL A 2012年第5期48卷 60-60页

作者： Carbone, D. Cappuzzello, F. Cavallaro, M. Ist Nazl Fis Nucl Lab Nazl Sud Via S Sofia 62 I-95125 Catania Italy Univ Catania Dipartimento Fis & Astron I-95125 Catania Italy

A general algorithm to extract the center of gravity of charge distributions generated in a patterned electrode is presented. The results obtained analyzing the data produced by the gas focal plane detector of the MAGNEX spectrometer are discussed. The algorithm is characterized by an unprecedented efficiency and accuracy when dealing with data with small signal-to-noise ratio. A clear recovery of the smooth position distribution from the quantized one obtained with standard algorithms is observed. The optimized algorithm is also able to naturally mitigate the cross-talk phenomena that are quite strong in this kind of compact detectors.

关键词： plate detector optimization algorithms Center of gravity Gas detectors algorithms generalized algorithm Electrodes CHARGE DISTRIBUTION analysis of algorithm

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Artificial neural network in prediction of mixed-mode I/II fracture load

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2023年第1期248卷

作者： Bahrami, Bahador Talebi, Hossein Ayatollahi, Majid R. Khosravani, Mohammad Reza Iran Univ Sci & Technol Ctr Excellence Expt Solid Mech & Dynam Sch Mech Engn Fatigue & Fracture Res Lab Tehran Iran Univ Siegen Chair Prod Dev Paul Bonatz Str 9-11 D-57068 Siegen Germany

The current research demonstrates the application of artificial neural network (ANN) in predicting the fracture under mixed-mode I/II loadings. To this end, based on the analysis of the relative importance of different input factors, the crack parameters of modes I and II stress intensity factors (KI and KII), T-stress, mode I fracture toughness (KIc), and ultimate tensile strength (sigma u) are selected and used as input data to the ANN model. Sub-sequently, a large number of empirical data are used, different ANN models are trained and built with the help of Levenberg-Marquardt (LM), Bayesian regularization (BR), and Broyden-Fletcher-Goldfarb-Shanno (BFGS) opti-mization algorithms. Finally, the trial-and-error procedure is used to determine the optimal number of hidden layers and neurons. The amounts of mean absolute percent error (MAPE) for the optimized models with LM, BR, and BFGS algorithms are equal to 8.4%, 5.1%, and 6.3%, respectively. All three models (i.e., ANN-LM, ANN-BR, and ANN-BFGS), estimate the new testing data successfully with approximately 91%, 95%, and 93% accuracy, respectively. This paper shows the effectiveness and the potential wide range application of the data-driven based fracture predictions in comparison with the traditional physics-based criteria.

关键词： Artificial neural network (ANN) Mixed -mode I II loading Relative importance optimization algorithms

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Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder

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PHYSICS OF FLUIDS 2018年第4期30卷 047105-047105页

作者： Jin, Xiaowei Cheng, Peng Chen, Wen-Li Li, Hui Harbin Inst Technol Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Harbin 150090 Heilongjiang Peoples R China Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Heilongjiang Peoples R China Harbin Inst Technol Sch Civil Engn Harbin 150090 Heilongjiang Peoples R China Harbin Inst Technol Inst Turbulence Noise Vibrat Interact & Control Shenzhen Div Shenzhen 518055 Peoples R China

A data-driven model is proposed for the prediction of the velocity field around a cylinder by fusion convolutional neural networks (CNNs) using measurements of the pressure field on the cylinder. The model is based on the close relationship between the Reynolds stresses in the wake, the wake formation length, and the base pressure. Numerical simulations of flow around a cylinder at various Reynolds numbers are carried out to establish a dataset capturing the effect of the Reynolds number on various flow properties. The time series of pressure fluctuations on the cylinder is converted into a grid-like spatial-temporal topology to be handled as the input of a CNN. A CNN architecture composed of a fusion of paths with and without a pooling layer is designed. This architecture can capture both accurate spatial-temporal information and the features that are invariant of small translations in the temporal dimension of pressure fluctuations on the cylinder. The CNN is trained using the computational fluid dynamics (CFD) dataset to establish the mapping relationship between the pressure fluctuations on the cylinder and the velocity field around the cylinder. Adam (adaptive moment estimation), an efficient method for processing large-scale and high-dimensional machine learning problems, is employed to implement the optimization algorithm. The trained model is then tested over various Reynolds numbers. The predictions of this model are found to agree well with the CFD results, and the data-driven model successfully learns the underlying flow regimes, i.e., the relationship between wake structure and pressure experienced on the surface of a cylinder is well established. Published by AIP Publishing.

关键词： Artificial intelligence Fluid flows Computer simulation Machine learning Vortex dynamics Fluid dynamics Vector fields optimization algorithms Covariance and correlation Turbulence simulations

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Power system loading margin enhancement by optimal STATCOM integration - A case study

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COMPUTERS & ELECTRICAL ENGINEERING 2020年第0期81卷 106521-000页

作者： Sreedharan, Sasidharan Joseph, Tibin Joseph, Sebin Chandran, Chittesh Veni Vishnu, J. Das, Vipin P. MES Coll Engn Kuttippuram Kerala India Cardiff Univ Sch Engn Cardiff CF24 3AA S Glam Wales Ahleia Switchgear Co Kuwait Kuwait Dublin Inst Technol Dublin Ireland Kerala State Elect Board Thiruvananthapuram Kerala India Motilal Nehru Natl Inst Technol Allahabad Uttar Pradesh India

Safe and secure network operation with acceptable voltage level has become a challenging task for utilities requiring corrective measures to be implemented. Network upgrades using Flexible Alternating Current Transmission System devices are being considered to serve this purpose. To this end, static loading margin enhancement by optimal static synchronous compensator (STATCOM) allocation to enhance the power transfer capability with minimal voltage variation is presented. Maximum loadability is formulated as an optimization problem, subjected to voltage and small -signal stability constraints. Stability indices are presented and incorporated with the optimization problem to ensure secure operation under maximum loading. The scheme is executed with the IEEE system and an Indian utility network. Improved voltage regulation with different loading condition was achieved for both test networks, with the service rendered by the optimally placed STATCOM. Moreover, it facilitates an additional 50% capacity release in both test systems for hosting the active power and loads. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： optimization algorithms Genetic algorithm Optimal placement Loading margin Hosting capacity Power system security and stability

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Systematic search and mapping review of the concrete delivery problem (CDP): Formulations, objectives, and data

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AUTOMATION IN CONSTRUCTION 2023年 145卷

作者： Tzanetos, Alexandros Blondin, Maude Univ Sherbrooke Fac Engn Dept Elect Engn & Comp Engn Multiobject Optimizat Res LabMORE Lab 2500 Blvd Univ Sherbrooke Sherbrooke PQ J1K 2R1 Canada

Over the last decades, researchers have studied one of the most complex combinatorial problems, i.e., the Concrete Delivery Problem (CDP), set different formulations of it, considered several objectives, and suggested various approaches. But no work has gathered this information to facilitate the comprehensive study of the CDP and investigate the research paths taken until today. Using a systematic search and mapping review, this paper fills that gap by (a) performing a comprehensive search on the CDP, and (b) mapping out and categorizing the existing literature. Specifically, this paper provides an overview of the various methods of the literature, categorizing the problem formulations based on the literature's different concepts, i.e., Integer Programming (IP), Time-Space Network, and the Job Shop model. The consistency between industry needs and existing constraints was discussed, and insights into the data sets corresponding to real-world cases were provided, identifying the necessary data for practitioners.

关键词： Concrete delivery problem Ready -mixed concrete delivery problem Problem formulation optimization algorithms Combinatorial optimization Systematic search and mapping review Data

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Effects of dispatch algorithms and fuel variation on techno-economic performance of hybrid energy system in remote island

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JOURNAL OF ENERGY STORAGE 2024年 78卷

作者： Sakib, Nazmus Hoque, Md Emdadul Rashid, Fazlur Aziz, Muhammad Uddin, Md Shehab Islam, Md. Tariqul Rajshahi Univ Engn & Technol Dept Mech Engn Rajshahi 6204 Bangladesh Missouri Univ Sci & Technol Dept Mech & Aerosp Engn Rolla MO 65401 USA Univ Tokyo Inst Ind Sci 4-6-1 KomabaMeguro Ku Tokyo 1538505 Japan

A stand-alone hybrid energy system is needed to mitigate the growing demand for future energy and to reduce emissions generated by conventional fuel sources. This work shows and proposes an optimum hybrid energy system configuration with less environmental pollution for a coastal region of Bangladesh with a load demand of 292.20 kWh/d and a peak load of 41.14 kW. This research presents the cost of energy (COE) and net present cost (NPC) for a PV-wind hybrid system with five alternative fuel generator technologies. For each possible configuration, the effects of three alternative dispatch algorithms, load following (LF), cycle charging (CC), and combined dispatch (CD), are examined. All three algorithms were validated using a genetic algorithm (GA), Cuckoo search algorithm (CUSA), Constrained Particle Swarm optimization (CPSO), Harmony search algorithm (HSA), and Non-dominated Sorting Genetic Algorithm (NSGA-II). Furthermore, a sensitivity analysis is also conducted by utilizing the gasoline price, discount rate, battery cost, PV cost, and inflation rate. The results show that the PV-wind-natural gas-based system provides the minimum COE (0.196 USD/kWh) and NPC (270,483 USD) when the CD algorithm is followed. For the same optimal hybrid energy system, the COE is 0.201 USD/ kWh, 0.0998 USD/kWh, 0.101 USD/kWh, 0.101 USD/kWh, and 0.0987 USD/kWh respectively for GA, CUSA, CPSO, HSA, and NSGA-II. However, COE increases by 8 % (0.212 USD/kWh) and 15 % (0.225 USD/kWh) when the CC and LF algorithms are followed. In addition, a comparison of all configurations reveals that the PV-windbiomass configuration with the CD algorithm excludes biomass generators to lower the COE, making the system emission-free.

关键词： Hybrid energy system Smart grid Renewable energy Cost of energy (COE) Net present cost (NPC) optimization algorithms

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Turning Functions in Optimal Synthesis of Mechanisms

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JOURNAL OF MECHANICAL DESIGN 2015年第6期137卷 062302-062302页

作者： Nadal, Fernando Cabrera, Juan A. Bataller, Alex Castillo, Juan J. Ortiz, Antonio Univ Malaga Dept Mech Engn E-29071 Malaga Spain

In this paper, we describe the use of turning functions to compare errors between the coupler and the target paths. The main reason to use turning functions is that the measured error does not depend on the mechanism scale or the position and rotation of the fixed link. Therefore, the searching space for the optimization algorithm is reduced. To carry out mechanism synthesis, we use an evolutionary algorithm. The effectiveness of the proposed method has been demonstrated in five synthesis examples.

关键词： Turning algorithms Errors Shapes Design optimization algorithms

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Detection of malicious Android applications using Ontology-based intelligent model in mobile cloud environment

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021年 58卷

作者： Nisha, Jannath O. S. Bhanu, Mary Saira S. Natl Inst Technol Dept Comp Sci & Engn Thiruchirappalli Tamil Nadu India

Mobile Cloud Computing (MCC) is a computing model that makes mobile devices resourceful by executing mobile applications (apps) in the cloud and storing data in cloud servers. MCC faces several security threats in both the Cloud and Mobile environments. Among several threats, malicious apps are the most threatening ones, because they can perform various malicious activities in both environments. The traditional malware detection methods may not detect new types of malware or rapidly changing malware behavior. So, there is a need to develop an accurate model for detecting malicious apps in the MCC environment. Scalability and Knowledge Reusability are challenging issues in existing detection methods. To overcome these issues, the proposed model uses an effective Ontology-based intelligent model based on app permissions to detect malware apps. This model extracts the relationship between the static features from the apps and builds an Apps Feature Ontology (AFO). A concept vector set for apps is created using the items obtained from the AFO. The most discriminant features are selected using optimization algorithms like Particle Swarm optimization, Social Spider Algorithm (SSA), and Gravitational Search Algorithm to reduce the dimension of the concept vector set. Various classifiers are applied to the reduced set. The efficiency of the proposed approach was evaluated on datasets obtained from the AndroZoo repository and VirusShare. The experimental results reveal that the proposed model can correctly detect malware using the Random Forest (RF) classifier with SSA and achieve higher detection accuracy with the lesser fall-out and less detection speed than existing Android malware detection techniques. Specifically, RF with SSA obtained higher accuracy, F1-score, and reduction in the fall-out of 94.11%, 93%, and 3%, respectively.

关键词： Mobile Cloud Computing Malware and Benign apps Ontology optimization algorithms Machine Learning classifiers

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Decision-making and optimal design of green energy system based on statistical methods and artificial neural network approaches

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SUSTAINABLE CITIES AND SOCIETY 2022年 84卷

作者： Samy, M. M. Almamlook, Rabia Emhamed Elkhouly, Heba I. Barakat, Shimaa Beni Suef Univ Fac Engn Dept Elect Engn Bani Suwayf 62511 Egypt Western Michigan Univ Dept Ind Engn & Engn Management Kalamazoo MI USA Al Zawiya Univ Dept Aviat Engn Al Zawiya City Libya Beni Suef Univ Fac Engn Dept Mech Engn Bani Suwayf Egypt

This study attempts to establish a novel statistical and ANN-based decision-making mechanism for appraising hybrid system optimization strategies. An operational strategy and optimization problem for a hybrid, off-grid PV-wind system based on Nickel Iron battery storage is established in this paper, and an objective function for the system under consideration is presented. Particle swarm optimization, hybrid firefly and harmony search algorithm (HFAHS), Cultural Algorithm, harmony search, and Simulated Annealing are all used to address the optimization issue. A new procedure for selecting the optimal efficient optimization algorithm based on the OneWay ANOVA, the Tukey test, and ANN has been suggested, which allows for effective comparative analysis of algorithms. In the proposed approach, there were just a few steps to determine which algorithm would be the most successful in addressing the specific problem. A comparison of meta-heuristic optimization techniques based on ANN for the optimality of the presented system has been done. Comparing statistical parameters and ANN (i.e., MAE, MAPE, RMSE, and R-squared) to other models verifies the proposed model's efficiency. PSO's Rsquared was 99.7%, indicating more accurate predictions. Although the ANN is superior, the PSO algorithm surpasses all other statistically evaluated algorithms.

关键词： Off -grid green energy system Tukey test Kolmogorov-Smirnov test Decision -making method Artificial Neural Network optimization algorithms One-WayANOVA

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