Estimation of gross calorific value of coals using artificial neural networks
By Shagufta U. Patel, B. Kumar, Y. Badhe, B. Sharma, S. Saha, S. Biswas, A. Chaudhury, S. Tambe, Bhaskar D. Kulkarni. 2007
142 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Biomedical Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/5a547fca91ff9956b5b38a8d89b790c9773ef282
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Mechanical Engineering
Citations in the corpus
- Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network (2010, background): “The calorific value is usually expressed as the higher heating value or gross calorific value (GCV) (Patel et al., 2007).”
- Estimation of gross calorific value based on coal analysis using regression and artificial neural networks (2009, background): “However, the numbers of nodes in hidden layers (J, K, L and M) are adjustable parameters, whose magnitudes are governed by issue such as the desired prediction accuracy and generalization performance of ANN model (Patel et al., 2007).”
- Explaining the relationship between common coal analyses and Afghan coal parameters using statistical modeling methods (2013, methodology): “The heating value is usually expressed as the higher heating value or gross calorific value (GCV) [17].”
- Estimation of free-swelling index based on coal analysis using multivariable regression and artifici (2011, background): “Similar weighted connections exist between nodes of hidden layers, as well as between the last hidden layer and nodes in the output layer [18–23].”