[HTML][HTML] Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts
Pesticide pollution has been posing a significant risk to human and ecosystems, and
photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) …
photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) …
Estimation of CO2 adsorption in high capacity metal− organic frameworks: Applications to greenhouse gas control
In recent decades, adsorption of high amounts of carbon dioxide (CO 2) in metal-organic
frameworks (MOFs) has recived attention and is studied broadly. As a main principle, most …
frameworks (MOFs) has recived attention and is studied broadly. As a main principle, most …
Towards estimating absorption of major air pollutant gasses in ionic liquids using soft computing methods
Capture of air pollutant gases using novel and green solvents is obtaining widespread
attention. Accurate estimation of this process is complex. We have estimated the absorption …
attention. Accurate estimation of this process is complex. We have estimated the absorption …
Insights into the estimation of heavy metals ions sorption from aqueous environment onto natural zeolite
In this work, we present how soft computing approaches can be used to study the sorption
performance of natural zeolite to eliminate heavy metals ions including Zn 2+, Ni 2+, Cd 2+ …
performance of natural zeolite to eliminate heavy metals ions including Zn 2+, Ni 2+, Cd 2+ …
Estimating flashpoints of fuels and chemical compounds using hybrid machine-learning techniques
Flashpoint of organic materials is a crucial physical property in industrial applications and
laboratory experiments, which provides information on safety standards and needed …
laboratory experiments, which provides information on safety standards and needed …
[HTML][HTML] Modeling and estimation of CO2 capture by porous liquids through machine learning
Porous liquids (PLs) are newly developed porous materials that combine unique fluidity with
permanent porosity, which exhibit promising functionalities. They have shown ability to …
permanent porosity, which exhibit promising functionalities. They have shown ability to …
Experimental analysis of combustion characteristics of corn starch dust clouds under the action of unilateral obstacles and machine learning modeling based on PSO …
J Zhang, X Cao, C Li, Z Du, S Bao, G Li… - Advanced Powder …, 2024 - Elsevier
Corn starch powder is highly flammable and explosive, presenting significant safety hazards
of dust explosions when encountering obstacles during its production and processing. This …
of dust explosions when encountering obstacles during its production and processing. This …
Molecular descriptors-based models for estimating net heat of combustion of chemical compounds
The heating values of fuels are determined by Heat of Combustion (Δ HC∘) in which the
higher amount is more lucrative. Moreover, one of the best methods to compare the …
higher amount is more lucrative. Moreover, one of the best methods to compare the …
New structure-based models for the prediction of flash point and autoignition temperatures of alkyl esters
Z Heidari, MA Sobati - Chemometrics and Intelligent Laboratory Systems, 2023 - Elsevier
In this study, new models based on quantitative structure-property relationship (QSPR) have
been proposed for the prediction of autoignition temperature (AIT) and flash point (FP) of …
been proposed for the prediction of autoignition temperature (AIT) and flash point (FP) of …
Evaluation of the flammability characteristics of alkyl esters: New QSPR models
Z Heidari, MA Sobati - Journal of Molecular Liquids, 2023 - Elsevier
In the present study, new quantitative structure–property relation (QSPR) models have been
developed to predict different flammability characteristics (ie, Lower flammability …
developed to predict different flammability characteristics (ie, Lower flammability …