Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite Article Swipe

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Bahareh Tanhaei , Morteza Esfandyari , Ali Ayati , Mika Sillanpää ·

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.1007/s40097-016-0211-4

In the present paper, the adaptive neural fuzzy inference system (ANFIS) was used for modeling of magnetic chitosan adsorption performance for the methyl orange removal. The ANFIS network, which is the best in data predicting, was trained with back propagation optimum method. Our results revealed that the developed ANFIS models can effectively model the non-linearity behavior of the adsorptive performance and the predicted values were in good agreement with the experimental data.

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Adaptive neuro fuzzy inference system Fuzzy inference system Nanochemistry Chitosan Nanocomposite Artificial neural network Adsorption Inference system Materials science Fuzzy logic Methyl orange Linearity Computer science Artificial intelligence Chemical engineering Nanotechnology Chemistry Fuzzy control system Engineering Electronic engineering Organic chemistry Photocatalysis Catalysis

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Type: article
Language: en
Landing Page: https://doi.org/10.1007/s40097-016-0211-4
PDF: https://link.springer.com/content/pdf/10.1007%2Fs40097-016-0211-4.pdf
OA Status: hybrid
Cited By: 23
References: 37
Related Works: 10
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DOI: https://doi.org/10.1007/s40097-016-0211-4

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Title: Neuro-fuzzy modeling to adsorptive performance of magnetic chitosan nanocomposite

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Type: article

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Language: en

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Publication year: 2016

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Publication date: 2016-11-23

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Authors: Bahareh Tanhaei, Morteza Esfandyari, Ali Ayati, Mika Sillanpää

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Landing page: https://doi.org/10.1007/s40097-016-0211-4

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Concepts: Adaptive neuro fuzzy inference system, Fuzzy inference system, Nanochemistry, Chitosan, Nanocomposite, Artificial neural network, Adsorption, Inference system, Materials science, Fuzzy logic, Methyl orange, Linearity, Computer science, Artificial intelligence, Chemical engineering, Nanotechnology, Chemistry, Fuzzy control system, Engineering, Electronic engineering, Organic chemistry, Photocatalysis, Catalysis

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Cited by: 23

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Citations by year (recent): 2025: 3, 2024: 4, 2023: 2, 2022: 5, 2021: 4

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Related works (count): 10

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