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Keywords: artificial neural network
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Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. October 2023, 145(10): 102302.
Paper No: JERT-22-1852
Published Online: April 17, 2023
... (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. December 2022, 144(12): 122302.
Paper No: JERT-22-1107
Published Online: June 6, 2022
... 2022 06 06 2022 design of experiment artificial neural network genetic algorithm Pareto-optimal front variable valve actuation heavy-duty engine air emissions from fossil fuel combustion fuel combustion Energimyndigheten 10.13039/501100004527 47119-1...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. September 2022, 144(9): 093002.
Paper No: JERT-21-2015
Published Online: February 9, 2022
... techniques: artificial neural network (ANN), support vector machine (SVM), and decision tree (DT); the second dataset was used to evaluate it. The ML results were compared with the results of a real-time drilling-data-quality expert. Despite the complexity of ANN and good results in general, it achieved...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. June 2022, 144(6): 061301.
Paper No: JERT-21-1247
Published Online: August 4, 2021
...Umang H. Rathod; Vinayak Kulkarni; Ujjwal K. Saha This article addresses the application of artificial neural network (ANN) and genetic expression programming (GEP), the popular artificial intelligence, and machine learning methods to estimate the Savonius wind rotor’s performance based...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. April 2022, 144(4): 043003.
Paper No: JERT-21-1461
Published Online: July 19, 2021
...Romy Agrawal; Aashish Malik; Robello Samuel; Amit Saxena The lithology of the formation is known to affect the drilling operation. Litho-facies help in the quantification of the formation properties, which optimizes the drilling parameters. The proposed work uses the artificial neural network...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. April 2022, 144(4): 042302.
Paper No: JERT-21-1196
Published Online: July 15, 2021
.... As direct combustion control is challenging, alternative methods like combustion physics-derived models are a subject of research interest. In this work, a composite predictive model was proposed by integrating trained random forest (RF) machine learning and artificial neural networks (ANNs) to combustion...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. January 2021, 143(1): 010901.
Paper No: JERT-20-1212
Published Online: August 24, 2020
...S. N. Pandey; M. Singh This work presents the prediction of thermal drawdown of an enhanced geothermal system (EGS) using artificial neural network (ANN). A three-dimensional numerical model of EGS was developed to generate the training and testing data sets for ANN. We have performed...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. November 2020, 142(11): 112109.
Paper No: JERT-20-1182
Published Online: June 25, 2020
... for the conduction of extensive parametric studies. Therefore, the simulations are used to train an artificial neural network. Comparing various algorithms of the artificial neural network, the radial basic function network is selected. The results show that variations in radiative heat transfer as well as those...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. October 2020, 142(10): 102001.
Paper No: JERT-20-1259
Published Online: June 12, 2020
...] Yarahmadi , M. , Mahan , R. , McFall , K. , and Barkhi Ashraf , A. , 2020 , “ Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network ,” Remote Sensing , 12 ( 1 ), p. 176 . 10.3390/rs12010176 [19] Ma , J. , Xu , S...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. December 2020, 142(12): 123301.
Paper No: JERT-19-1835
Published Online: June 12, 2020
... models applying artificial neural network (ANN) to predict hydrocarbon productions under heterogeneous and unknown properties of unconventional reservoirs. We study two different thermal recovery methods—expanding solvent steam-assisted gravity drainage for bitumen and in-situ upgrading of oil shale. We...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. October 2020, 142(10): 103007.
Paper No: JERT-19-1740
Published Online: June 9, 2020
... on nonlinear diffusion filters to constitute the dataset for machine learning. An artificial neural network (ANN) is then generated and trained to simulate the connection relationship between the producer and its adjacent injectors. The genetic algorithm (GA) is also introduced to avoid the tedious process...
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. May 2020, 142(5): 050902.
Paper No: JERT-19-1610
Published Online: December 10, 2019
... accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs...
Journal Articles
Publisher: ASME
Article Type: Research-Article
J. Energy Resour. Technol. April 2019, 141(4): 041001.
Paper No: JERT-18-1524
Published Online: November 19, 2018
...), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can...
Topics:
Artificial neural networks,
Carbon dioxide,
Diffusion (Physics),
Genetic algorithms,
Support vector machines,
Temperature,
Viscosity
Includes: Supplementary data