Selection of the most influential flow and thermal parameters for predicting the efficiency of activated carbon filters using neuro-fuzzy technique

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Selection of the most influential flow and thermal parameters for predicting the efficiency of activated carbon filters using neuro-fuzzy technique

Cited by 3 publications

References 16 publications

Predicting algal appearance on mortar surface with ensembles of adaptive neuro fuzzy models: a comparative study of ensemble strategies

Predicting algal appearance on mortar surface with ensembles of adaptive neuro fuzzy models: a comparative study of ensemble strategies

Application of hybrid support vector regression artificial bee colony for prediction of MMP in CO2-EOR process

Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation

Contact Info

Product

Resources

About