scholarly journals MLP-Based Model for Estimation of Methane Seam Pressure

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7661
Author(s):  
Marta Skiba ◽  
Barbara Dutka ◽  
Mariusz Młynarczuk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Direct Measurement ◽  
Sorption Isotherms ◽  
Indirect Measurement ◽  
Prediction Errors ◽  
Practical Application ◽  
Neural Models ◽  
Mining Operations ◽  
Best Fit

One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring, primarily technical difficulties associated with direct measurement and the time-consuming nature of indirect measurement, this parameter is often disregarded in the coal and gas outburst forecasts. To overcome the above-mentioned difficulties, an attempt was made to estimate the methane seam pressure with the use of artificial neural networks. Two MLP-based models were developed to estimate the average and maximum methane seam pressure values, respectively. The analyses demonstrated high correlation between the values indicated by the neural models and the reference values determined on the basis of sorption isotherms. According to the adopted fit criterion, the prediction errors for the best fit were 2.59% and 3.04% for the average and maximum seam pressure values, respectively. The obtained determination coefficients (exceeding the value of 0.99) confirmed the very good predictive abilities of the models. These results imply a great potential for practical application of the proposed method.

scholarly journals The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures

2015 ◽  
Vol 10 (1) ◽  
pp. 47-56
Author(s):  
Artur Duchaczek ◽  
Dariusz Skorupka
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Problems ◽  
Logistics Management ◽  
Computer Application ◽  
Computational Algorithms ◽  
Practical Application ◽  
Artificial Neural

Abstract In the area of logistics management both managers and engineers rely primarily on proven computational algorithms, for this reason, it is often difficult to convince them to the use of artificial neural networks in solving decision problems. The paper presents the possibilities of using the FANN library in building of a computer application applied in the area of logistics. The possibilities of the component are presented on the example of applications of artificial neural networks to estimate the capacity of transport vehicles based on their dimensions. The example presented in the work was solved with the use of a multi-network Layered Perceptron. The example depicted not only the possibility of using artificial neural networks for solving poorly structured tasks but also practical application of the TFannNetwork component

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

2013 ◽  
Vol 61 (3) ◽  
pp. 589-594 ◽  
Author(s):  
M. Luzar ◽  
Ł. Sobolewski ◽  
W. Miczulski ◽  
J. Korbicz
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Scale ◽  
Theoretical Background ◽  
Group Method ◽  
Prediction Errors ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
Artificial Neural

Abstract In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Group Method of Data Handling) are shown. The main advantages and disadvantages of using such types of neural networks are discussed. The prediction of corrections is performed using two methods: the time series analysis method and the regression method. The input data were prepared suitable for the above mentioned methods, based on two time series, ts1 and ts2. The designation of prediction errors for specified days and the influence of data quantity for the prediction error are considered. The paper consists of five sections. After Introduction, in Sec. 2, the theoretical background for different types of neural networks is presented. Section 3 shows data preparation for the appropriate type of neural network. The experimental results are presented in Sec. 4. Finally, Sec. 5 concludes the paper.

scholarly journals Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks

2019 ◽  
Vol 16 (8) ◽  
pp. 1406 ◽  
Author(s):  
Tutak ◽  
Brodny
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Methane Concentration ◽  
Mining Industry ◽  
Mining Area ◽  
Work Safety ◽  
Mining Operations ◽  
Mining Hazards ◽  
Artificial Neural

Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations and cause the risk of fire or explosion. Therefore, it is necessary to monitor and predict its concentration in the areas of ongoing mining exploitation. The paper presents the results of tests performed to improve work safety. The article presents the methodology of using artificial neural networks for predicting methane concentration values in one mining area. The objective of the paper is to develop an effective method for forecasting methane concentration in the mining industry. The application of neural networks for this purpose represents one of the first attempts in this respect. The method developed makes use of direct methane concentration values measured by a system of sensors located in the exploitation area. The forecasting model was built on the basis of a Multilayer Perceptron (MLP) network. The corresponding calculations were performed using a three-layered network with non-linear activation functions. The results obtained in the form of methane concentration prediction demonstrated minor errors in relation to the recorded values of this concentration. This offers an opportunity for a broader application of intelligent systems for effective prediction of mining hazards.

scholarly journals Neural network technique when distribution of vehicle component parts on the technological repair routes, taking into account their technical condition

2018 ◽  
Vol 170 ◽  
pp. 05011
Author(s):  
Valentin Krasovsky ◽  
Nina Krasovskaya ◽  
Victor Poptsov ◽  
Irina Nordman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Technical Condition ◽  
Probabilistic Methods ◽  
Practical Application ◽  
Repair Work ◽  
Vehicle Component ◽  
Repair Enterprise ◽  
Artificial Neural

Increase of repair efficiency is achieved due to the formation of centralized specialized production facilities which implement the vehicle component parts repair technique with the use of industrial technological processes to restore the technical state of the units and their components. In this case, the establishment of the expediency of sending the unit to repair, as well as the defining of volumes and nomenclature for necessary repair actions, should be performed at the stage of pre-repair diagnosis for each individual unit taking into account its actual technical condition. However, the effectiveness of pre-repair diagnosis using both deterministic and probabilistic methods of processing and analyzing the information obtained is significantly reduced by the presence of errors in the recognition of defects and the distribution of aggregates in accordance with the repair work variety preformed at the repair enterprise. Using promising cognitive technology based on neural networks it is possible to completely avoid the losses associated with the repetition of repair work. Therefore, the formation of scientific and methodological bases for the development, training and practical application of artificial neural networks in the subsystems of the pre-repair diagnosis of the repair fund of automobile vehicle omponent parts is an important and urgent task. The paper presents the results of analytical studies and a number of original techniques for the formation of scientific and methodological foundations for the development, training and practical application of artificial neural networks in the process of diagnosis the car vehicle component parts and special oil and gas equipment entering the centralized repair according to their technical condition

A Generic Method for Determining Moisture Sorption Isotherms of Cereal Grains and Legumes Using Artificial Neural Networks

2014 ◽  
Vol 37 (3) ◽  
pp. 308-316 ◽  
Author(s):  
Majdi Al-Mahasneh ◽  
Fahad Alkoaik ◽  
Ahmed Khalil ◽  
Ahmad Al-Mahasneh ◽  
Ahmed El-Waziry ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sorption Isotherms ◽  
Moisture Sorption ◽  
Cereal Grains ◽  
Moisture Sorption Isotherms ◽  
Artificial Neural

scholarly journals Assessment of Arias Intensity of historical earthquakes using modified Mercalli intensities and artificial neural networks

2011 ◽  
Vol 11 (12) ◽  
pp. 3097-3105 ◽  
Author(s):  
G-A. Tselentis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Historical Earthquakes ◽  
Forecast Model ◽  
Seismic Intensity ◽  
Nonlinear Behaviour ◽  
Neural Models ◽  
Arias Intensity ◽  
Data Synthesis ◽  
Artificial Neural

Abstract. This paper presents the development of a non-parametric forecast model based on artificial neural networks for the direct assessment of Arias Intensity corresponding to a historic earthquake using seismic intensity data. The neural models allow complex and nonlinear behaviour to be tracked. Application of this methodology on earthquakes with known instrumental data from Greece, showed that the artificial neural network forecast model have excellent data synthesis capability.

scholarly journals Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

2017 ◽  
Vol 60 (1) ◽  
pp. 175-189 ◽  
Author(s):  
Krzysztof Przednowek ◽  
Janusz Iskra ◽  
Krzysztof Wiktorowicz ◽  
Tomasz Krzeszowski ◽  
Adam Maszczyk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Radial Basis Function Network ◽  
Training Data ◽  
Neural Models ◽  
National Team ◽  
Novel Approach ◽  
Radial Basis ◽  
Artificial Neural

Abstract This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

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