A Cihan University-Erbil Professor Published a Research Article with Elsevier
Professor Salah I.Yahya from the Department of Communication and Computer Engineering at Cihan University-Erbil published a research article entitled “A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass ” in the International Journal of Molecular Sciences .
About the author:
Name: Salah I.Yahya
Qualification: Ph.D.
Academic rank: Professor
Affiliation: Department Communication and Computer Engineering, Cihan University-Erbil
TAP: https://sites.google.com/cihanuniversity.edu.iq/salah-yahya/home
Google Scholar Account: https://scholar.google.com/citations?hl=en&user=jXO8SZkAAAAJ
Journal Coverage:
https://www.mdpi.com/journal/ijms
Title: International Journal of Molecular Sciences
Science Citation Index: https://mjl.clarivate.com:/search-results?issn=1661-6596&hide_exact_match_fl=true&utm_source=mjl&utm_medium=share-by-link&utm_campaign=search-results-share-this-journal
Science Citation Index Expanded
Clarivate Analytics (Wos: IF = 7.69)
SCOPUS: Q1
Publisher: Elsevier GMBH
Country: United Kingdom
About the Paper:
Title: A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass
DOI: https://doi.org/10.3390/ijms24065780
Abstract:
The higher heating value (HHV) is the main property showing the energy amount of biomass samples. Several linear correlations based on either the proximate or the ultimate analysis have already been proposed for predicting biomass HHV. Since the HHV relationship with the proxi- mate and ultimate analyses is not linear, nonlinear models might be a better alternative. Accordingly, this study employed the Elman recurrent neural network (ENN) to anticipate the HHV of different biomass samples from both the ultimate and proximate compositional analyses as the model inputs. The number of hidden neurons and the training algorithm were determined in such a way that the ENN model showed the highest prediction and generalization accuracy. The single hidden layer ENN with only four nodes, trained by the Levenberg–Marquardt algorithm, was identified as the most accurate model. The proposed ENN exhibited reliable prediction and generalization performance for estimating 532 experimental HHVs with a low mean absolute error of 0.67 and a mean square error of 0.96. In addition, the proposed ENN model provides a ground to clearly understand the dependency of the HHV on the fixed carbon, volatile matter, ash, carbon, hydrogen, nitrogen, oxygen, and sulfur content of biomass feedstocks.
