AI Medical Compendium Journal:
Chemosphere

Showing 31 to 40 of 147 articles

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) prediction model based on limited peat samples using an evolved artificial neural network.

Chemosphere
Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high ...

Prediction of g-CN-based photocatalysts in tetracycline degradation based on machine learning.

Chemosphere
Investigating the effects of g-CN-based photocatalysts on experimental parameters during tetracycline (TC) degradation can be helpful in discovering the optimal parameter combinations to improve the degradation efficiencies in general. Machine learni...

Machine learning screening of biomass precursors to prepare biomass carbon for organic wastewater purification: A review.

Chemosphere
In the past decades, the amount of biomass waste has continuously increased in human living environments, and it has attracted more and more attention. Biomass is regarded as the most high-quality and cost-effective precursor material for the prepara...

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques.

Chemosphere
The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in ...

Predicting the Occurrence of Substituted and Unsubstituted, Polycyclic Aromatic Compounds in Coking Wastewater Treatment Plant Effluent using Machine Learning Regression.

Chemosphere
Organic contaminants such as polycyclic aromatic compounds (PACs) occurring in industrial effluents can not only persist in wastewater but transform into more toxic and mobile, substituted heterocyclic products during treatment. Thus, predicting the ...

RSM and ANN methodologies in modeling the enhanced biodiesel production using novel protic ionic liquid anchored on g-CN@FeO nanohybrid.

Chemosphere
Herin, a new nanohybrid acid catalyst was fabricated for the efficient biodiesel production. At the first, magnetic porous nanosheets of graphitic carbon nitride (g-CN@FeO) was prepared and then functionalized with sulfonic acid. Next, the preparatio...

Adsorptive removal of perfluorooctanoic acid from aqueous matrices using peanut husk-derived magnetic biochar: Statistical and artificial intelligence approaches, kinetics, isotherm, and thermodynamics.

Chemosphere
Removal of perfluorooctanoic acid (PFOA) from water matrices is crucial owing to its pervasiveness and adverse ecological and human health effects. This study investigates the adsorptive removal of PFOA using magnetic biochar (MBC) derived from FeCl-...

Loss of micropollutants on syringe filters during sample filtration: Machine learning approach for selecting appropriate filters.

Chemosphere
Prefiltration before chromatographic analysis is critical in the monitoring of environmental micropollutants (MPs). However, in an aqueous matrix, such monitoring often leads to out-of-specification results owing to the loss of MPs on syringe filters...

Data-driven, explainable machine learning model for predicting volatile organic compounds' standard vaporization enthalpy.

Chemosphere
The accurate prediction of standard vaporization enthalpy (ΔH°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental metho...