[HTML][HTML] Machine learning for expediting next-generation of fire-retardant polymer composites

P Jafari, R Zhang, S Huo, Q Wang, J Yong… - Composites …, 2023 - Elsevier
Abstract Machine learning algorithms have emerged as an effective and popular decision-
making tool for solving complicated engineering problems and challenges. Although …

A reliable model to predict the methane-hydrate equilibrium: An updated database and machine learning approach

M Hosseini, Y Leonenko - Renewable and Sustainable Energy Reviews, 2023 - Elsevier
Gas hydrates are a type of crystalline compounds that consists of water and small gas
molecules. A wide range of applications of gas hydrates in storing natural gas in the form of …

Machine learning prediction of specific capacitance in biomass derived carbon materials: Effects of activation and biochar characteristics

X Yang, C Yuan, S He, D Jiang, B Cao, S Wang - Fuel, 2023 - Elsevier
The preparation process of biomass-based biochar materials is usually screened using
traditional trial-and-error experiments. In this approach, the electrochemical properties of …

Auto-MatRegressor: liberating machine learning alchemists

Y Liu, S Wang, Z Yang, M Avdeev, S Shi - Science Bulletin, 2023 - Elsevier
Abstract Machine learning (ML) is widely used to uncover structure–property relationships of
materials due to its ability to quickly find potential data patterns and make accurate …

[HTML][HTML] Three phase equilibria of the methane hydrate in NaCl solutions: A simulation study

S Blazquez, C Vega, MM Conde - Journal of Molecular Liquids, 2023 - Elsevier
Molecular dynamics simulations have been performed to determine the three-phase
coexistence temperature for a methane hydrate system in equilibrium with a NaCl solution …

Accelerated design of flame retardant polymeric nanocomposites via machine learning prediction

Z Zhang, Z Jiao, R Shen, P Song… - ACS Applied Engineering …, 2022 - ACS Publications
Improving the flame retardancy of polymeric materials used in engineering applications is an
increasingly important strategy for limiting fire hazards. However, the wide variety of flame …

In-situ multi-phase flow imaging for particle dynamic tracking and characterization: Advances and applications

J Liu, W Kuang, J Liu, Z Gao, S Rohani… - Chemical Engineering …, 2022 - Elsevier
Real-time chemical process monitoring, analysis, and control have become increasingly
important to multi-phase flow process research and development and attracted overt …

[HTML][HTML] Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates

EL Gjelsvik, M Fossen, K Tøndel - Fuel, 2023 - Elsevier
Gas hydrates represent one of the main flow assurance challenges in the oil and gas
industry as they can lead to plugging of pipelines and process equipment. In this paper we …

An insight into the prediction of scale precipitation in harsh conditions using different machine learning algorithms

R Yousefzadeh, A Bemani, A Kazemi… - SPE Production & …, 2023 - onepetro.org
Scale precipitation in petroleum equipment is known as an important problem that causes
damages in injection and production wells. Scale precipitation causes equipment corrosion …

Development of explicit models to predict methane hydrate equilibrium conditions in pure water and brine solutions: A machine learning approach

M Hosseini, Y Leonenko - Chemical Engineering Science, 2024 - Elsevier
An important phase in the design of processes involving gas hydrates is predicting the
hydrate formation conditions. In this study, three explicit correlations based on machine …