Seismic energy prediction to optimize rock fragmentation: a modified approach

2021 ◽  
Author(s):  
A. Agrawal ◽  
B. S. Choudhary ◽  
V. M. S. R. Murthy
Keyword(s):  
Seismic Energy ◽  
Rock Fragmentation ◽  
Energy Prediction

Energy Supply Management Using the Solar Energy Prediction in Smart Grid

2016 ◽  
Vol 11 (5) ◽  
pp. 486
Author(s):  
Abdelilah Kahaji ◽  
Rachid Alaoui ◽  
Sadik Farhat ◽  
Lahoussine Bouhouch
Keyword(s):  
Solar Energy ◽  
Smart Grid ◽  
Energy Supply ◽  
Supply Management ◽  
Energy Prediction

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Energy Prediction Using Data Analytics in Smart Grid

2018 ◽  
Vol 06 (06) ◽  
pp. 110-115
Author(s):  
Panchami Anil ◽  
Anas P V ◽  
Naseef Kuruvakkottil ◽  
Anusha K V ◽  
Balagopal N
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Prediction ◽  
Using Data

scholarly journals A New Photovoltaic Module Efficiency Model for Energy Prediction and Rating

2021 ◽  
Vol 11 (2) ◽  
pp. 527-534
Author(s):  
Anton Driesse ◽  
Marios Theristis ◽  
Joshua S. Stein
Keyword(s):  
Photovoltaic Module ◽  
Energy Prediction ◽  
Module Efficiency

scholarly journals Influence of in-situ stress on rock fragmentation by TBM cutters and prediction of TBM performance for deep tunnels

2020 ◽  
Vol 570 ◽  
pp. 052025
Author(s):  
Hongsu Ma ◽  
Lijun Yin ◽  
Qiuming Gong ◽  
Ju Wang ◽  
Liang Chen
Keyword(s):  
In Situ Stress ◽  
Rock Fragmentation ◽  
Tbm Performance

scholarly journals Development of a Transient Energy Prediction Model for Machine Tools

Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 678-683
Author(s):  
Shailendra Pawanr ◽  
Girish Kant Garg ◽  
Srikanta Routroy
Keyword(s):  
Prediction Model ◽  
Machine Tools ◽  
Energy Prediction ◽  
Transient Energy
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