Metaheuristic design of feedforward neural networks: A review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …
key interest among the researchers and practitioners of multiple disciplines. The FNN …
Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
Mathematical modeling and simulation methods are important tools in studying various
processes in science and engineering. In the current review, we focus on the applications of …
processes in science and engineering. In the current review, we focus on the applications of …
Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculus
Lithium-ion batteries have emerged as the state-of-the-art energy storage for portable
electronics, electrified vehicles, and smart grids. An enabling Battery Management System …
electronics, electrified vehicles, and smart grids. An enabling Battery Management System …
Deep learning for seismic lithology prediction
Seismic prediction has been a huge challenge because of the great uncertainties contained
in the seismic data. Deep learning (DL) has been successfully applied in many fields and …
in the seismic data. Deep learning (DL) has been successfully applied in many fields and …
Reconstruction, optimization, and design of heterogeneous materials and media: Basic principles, computational algorithms, and applications
Modeling of heterogeneous materials and media is a problem of fundamental importance to
a wide variety of phenomena with applications to many disciplines, ranging from condensed …
a wide variety of phenomena with applications to many disciplines, ranging from condensed …
[HTML][HTML] Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs
This paper deals with the comparison of models for predicting porosity and permeability of
oil reservoirs by coupling a machine learning concept and petrophysical logs. Different …
oil reservoirs by coupling a machine learning concept and petrophysical logs. Different …
Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO-LSSVM models
One of the major properties of biodiesel fuels is cetane number (CN) which expresses the
ignition characteristics and quality of motor power. The main idea of this work was proposing …
ignition characteristics and quality of motor power. The main idea of this work was proposing …
Application of artificial intelligence techniques in the petroleum industry: a review
In recent years, artificial intelligence (AI) has been widely applied to optimization problems
in the petroleum exploration and production industry. This survey offers a detailed literature …
in the petroleum exploration and production industry. This survey offers a detailed literature …
Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data
AK Mulashani, C Shen, BM Nkurlu, CN Mkono… - Energy, 2022 - Elsevier
Permeability is the key variable for reservoir characterization used for estimating the flow
patterns and volume of hydrocarbons. Modern computer advancement has highlighted the …
patterns and volume of hydrocarbons. Modern computer advancement has highlighted the …
Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir
Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in
numerous numbers of oil production applications like those in remote or unmanned …
numerous numbers of oil production applications like those in remote or unmanned …