Artificial intelligence-based neural network modeling of adsorptive removal of phenol from aquatic environment Article Swipe
Bello Abdu Isah
,
Muthamilselvi Ponnuchamy
,
B. Senthil Rathi
,
P. Senthil Kumar
,
Ashish Kapoor
,
Manjula Rajagopal
,
Anjali Awasthi
,
Gayathri Rangasamy
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.dwt.2024.100564
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.dwt.2024.100564
In this study, adsorptive uptake of phenol from aqueous system using Arachis hypogaea (groundnut) shell-based adsorbent was experimentally investigated and modelled using artificial intelligence-based neural network approach. Artificial neural networks with different number of neurons were designed using Levenberg-Marquardt algorithm to find the best model for phenol adsorption. The feedforward back propagation neural network comprising TRAINLM, LARNGDM and TANSIG as training, adaptation learning and transfer functions, respectively, with ten neurons in the hidden layer exhibited the optimal architecture with the strongest correlation R2=0.9901 and the smallest mean square error MSE=0.045. The studies indicated a maximum adsorptive uptake of phenol to be 37.31 mg/g onto the activated shell powder. The kinetic analysis favored pseudo second order R2=0.9999and the equilibrium data was best represented by Freundlich isotherm modelR2=0.9976. Phenolic remediation phenomenon ensued in a spontaneous manner, was exothermic ∆H0=−34.25kJ/moland involved physisorption. The experimental results are agreement with model.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.dwt.2024.100564
- OA Status
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- Cited By
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https://doi.org/10.1016/j.dwt.2024.100564Digital Object Identifier
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Artificial intelligence-based neural network modeling of adsorptive removal of phenol from aquatic environmentWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-06-21Full publication date if available
- Authors
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Bello Abdu Isah, Muthamilselvi Ponnuchamy, B. Senthil Rathi, P. Senthil Kumar, Ashish Kapoor, Manjula Rajagopal, Anjali Awasthi, Gayathri RangasamyList of authors in order
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https://doi.org/10.1016/j.dwt.2024.100564Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1016/j.dwt.2024.100564Direct OA link when available
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Artificial neural network, Phenol, Environmental science, Computer science, Chemistry, Artificial intelligence, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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| referenced_works | https://openalex.org/W6735313909, https://openalex.org/W2346868242, https://openalex.org/W2582499918, https://openalex.org/W3193130158, https://openalex.org/W2900705110, https://openalex.org/W2580561717, https://openalex.org/W2772123271, https://openalex.org/W2780137270, https://openalex.org/W2887328484, https://openalex.org/W2913613288, https://openalex.org/W2505574534, https://openalex.org/W2408089261, https://openalex.org/W2055892691, https://openalex.org/W3157149188, https://openalex.org/W3094817679, https://openalex.org/W3156367570, https://openalex.org/W6848987972, https://openalex.org/W2036168959, https://openalex.org/W2905424314, https://openalex.org/W2033581663, https://openalex.org/W2903463505, https://openalex.org/W2958807243, https://openalex.org/W2885847622, https://openalex.org/W2946150933, https://openalex.org/W2793163001, https://openalex.org/W2517312548, https://openalex.org/W2000070747, https://openalex.org/W2901135138, https://openalex.org/W2888315679, https://openalex.org/W2793084056, https://openalex.org/W3005043396, https://openalex.org/W2419126454, https://openalex.org/W2063222659, https://openalex.org/W3157983468, https://openalex.org/W2218584056, https://openalex.org/W3014942619, https://openalex.org/W2996685422, https://openalex.org/W2900346612, https://openalex.org/W2753801052, https://openalex.org/W2946268517, https://openalex.org/W2790654086, https://openalex.org/W2204158801, https://openalex.org/W2907441693, https://openalex.org/W2912655651, https://openalex.org/W6739357010, https://openalex.org/W1964956955, https://openalex.org/W3010919095, https://openalex.org/W2075855774, https://openalex.org/W2061582892, https://openalex.org/W2072859058, https://openalex.org/W1815281117, https://openalex.org/W6780089730, https://openalex.org/W2761012527, https://openalex.org/W2291603953, https://openalex.org/W1616656956, https://openalex.org/W2584388489, https://openalex.org/W6676614789, https://openalex.org/W2047063672, https://openalex.org/W2589662406, https://openalex.org/W3213631024, https://openalex.org/W2597225184, https://openalex.org/W3212251833, https://openalex.org/W4317491706, https://openalex.org/W2626796158, https://openalex.org/W2111341526 |
| referenced_works_count | 65 |
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| corresponding_author_ids | https://openalex.org/A5049372903, https://openalex.org/A5040166011 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I101407740, https://openalex.org/I145286018, https://openalex.org/I56306041 |
| citation_normalized_percentile.value | 0.8459232 |
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