Machine learning has seen increasing implementation as a predictive tool in the chemical
and physical sciences in recent years. It offers a route to accelerate the process of scientific …

Artificial neural networks (ANN) provide a range of powerful new techniques for solving
problems in sensor data analysis, fault detection, process identification, and control and …

Nowadays, supercritical fluids (SCFs) technologies present a processing option for
obtaining new products with noteworthy characteristics. Access to reliable solubility data of …

Abstract In Discrete Element Method (DEM) simulations, particle–particle contact laws
determine the macroscopic simulation results. Particle-based contact laws, in turn …

The solubility of Azathioprine, as an immunosuppressive and anti-cancer drug, in
supercritical carbon dioxide (SC-CO 2) was measured for the first time. Under the applied …

This paper presents the best artificial neural network (ANN) model for the estimation of the
convective heat transfer coefficient (HTC) of nanofluids flowing through a circular tube with …

The solubility of sunitinib malate, an anti-cancer medicine, in supercritical carbon dioxide
(SC–CO 2) was measured for the first time. Under the applied conditions in terms of …

In this work, a nonlinear model that integrates the group contribution (GC) method with a
well-known machine learning algorithm, ie, artificial neural network (ANN), is proposed to …

The clathrate hydrate formation in pipelines and treatment systems of gas and natural gas
liquid (NGL) is an undesirable operating phenomenon. It interrupts the gas flow continuity …

Among available methods, supercritical fluid (SCF) technology is a new and suitable
approach for producing nano-sized particles. For this purpose, determination of a solid …