Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Abdulrahman Sumayli"'
Publikováno v:
Ain Shams Engineering Journal, Vol 15, Iss 8, Pp 102834- (2024)
We proposed a new computational methodology for computational modeling of mass transfer in membranes with application for molecular separation. The computational method centered on hybrid computational fluid dynamics (CFD) and machine learning (ML) i
Externí odkaz:
https://doaj.org/article/6ca0f9a228b0486b8ea8c5e3d9d2bdff
Publikováno v:
Case Studies in Thermal Engineering, Vol 53, Iss , Pp 103856- (2024)
Industrial application of novel, green and effective liquid absorbents for improving the separation yield of CO2 greenhouse pollutant is an important milestone to reduce its emission to the atmosphere. In this research, the separation performance of
Externí odkaz:
https://doaj.org/article/126215fe0b494e7392dcd373dc34ccbc
Publikováno v:
Case Studies in Thermal Engineering, Vol 51, Iss , Pp 103587- (2023)
This research focuses on investigating the solubility of tolfenamic acid in SC-CO2 (supercritical carbon dioxide) and the density of SC-CO2 solvent via theoretical artificial intelligence method. The study involves analyzing the relationship between
Externí odkaz:
https://doaj.org/article/65fb7ef6e09944c3b9465b677d15a188
Publikováno v:
Case Studies in Thermal Engineering, Vol 50, Iss , Pp 103444- (2023)
In thermal systems, combination of PCMs with porous fillers with high thermal conductivity is a proper method to improve their performance. In photovoltaic thermal (PVT) systems, as porous fillers are introduced to the collector tube and PCM bulk, th
Externí odkaz:
https://doaj.org/article/d913f29847e2467f8df53b89cfa43e82
Publikováno v:
Arabian Journal of Chemistry, Vol 16, Iss 7, Pp 104801- (2023)
In this study, different distinct approaches of machine learning (ML) including Multi-layer perceptron (MLP), Gradient Boosting with DT (GBDT), and Gaussian process regression (GPR) were employed in order to predict the amount of Papaya oil methyl es
Externí odkaz:
https://doaj.org/article/bdf5a7a918264b5ea8f50a69d43a724a
Autor:
Abdulrahman Sumayli
Publikováno v:
Arabian Journal of Chemistry, Vol 16, Iss 7, Pp 104833- (2023)
Data-driven machine learning (ML) methods are extensively employed for modeling and simulation of highly complicated processes. ML techniques confirmed their great predictive capability compared to conventional techniques for modeling and management
Externí odkaz:
https://doaj.org/article/c91e961a8d584b9ab0e4c1445ee78d69