Experimental study and analysis of CO and SO absorption in various water-based nanofluids by response surface methodology.

The absorption of acidic gases in the oil and gas industries is important due to their toxicity and corrosive effects. Recently, the application of nanofluids based on aqueous or organic solvents as absorbents has been examined by a variety of researchers. In this study, a single bubble column was exploited to study the effect of water-based nanofluids on the absorption processes of SO and CO using response surface methodology (RSM) based on Box-Behnken three-level experiment design.

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches.

Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts.