Predictive calculation of carbon dioxide solubility in polymers

Abstract

Novel calculation model of CO2 solubility in polymers using a hybrid intelligence algorithm.

On the evaluation of the carbon dioxide solubility in polymers using gene expression programming

“…CO 2 solubility is the maximum CO 2 quantity that can solute in different solutions. Evaluation, prediction, and measurement of CO 2 solubility in different biodegradable polymers has become notable technology for engineers in various chemical applications such as extraction and generation of novel materials 10 – 14 . Biodegradable polymers are a particular type of polymers that collapse by bacterial dissolution process to eventuate in natural fluids such as CO 2 and N 2 .…”

“…At the same year, Minelli and Sarti 34 measured solubility and permeability of CO 2 in various glassy polymers by considering diffusion coefficient as a kinetic factor. In 2015, different mathematical and theoretical approaches by Ting and Yuan 10 , Li et al 7 and Quan et al 12 were studied to estimate CO 2 properties including solubility. All of them showed that the CO 2 solubility has direct relation with pressure and reverse relation with temperature.…”

Estimating Gas Sorption in Polymeric Membranes from the Molecular Structure: A Machine Learning Based Group Contribution Method for the Non- Equilibrium Lattice Fluid Model (Ml-Gc-Nelf)

“…With the advent of machine learning (ML) as a powerful correlation tool, many data-driven models were built to predict various transport and thermophysical properties of polymeric materials [43][44][45][46][47][48]. Li et al [43] have built a backpropagation artificial neural network (ANN) to estimate the solubility of CO 2 and N 2 in polystyrene, and CO 2 in polypropylene.…”

Prediction of supercritical carbon dioxide solubility in polymers based on hybrid artificial intelligence method integrated with the diffusion theory

“…Hussain et al 31 proposed the mixed neural network solution calculation model by combining Kent-Eisenberg with ANN and realized the better prediction performance. Li et al [32][33][34][35][36][37][38] also proposed several dissolution prediction models by combining chaos theory and particle swarm algorithm with the clustering method, improved the ANN training algorithm, and obtained the better prediction accuracy and correlation.…”