Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass

Vinayagam, Ramesh and Dave, Niyam and Varadavenkatesan, Thivaharan and Rajamohan, Natarajan and Sillanappa, Mika and Nadda, Ashok Kumar and Govarthana, Muthuswamy and Selvaraj, Raja (2022) Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass. Chemosphere, 296. ISSN 0045-6535

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Official URL: http://www.elsevier.com/locate/chemosphere

Abstract

This study focused on the sustainable removal of chromium in its hexavalent form by adsorption using sugar�extracted spent marine macroalgal biomass – Ulva prolifera. The adsorption of Cr (VI) from aqueous solutions utilizing macroalgal biomass was studied under varying conditions of pH, adsorbent amount, agitation speed, and time to assess and optimize the process variables by using a statistical method – response surface method�ology (RSM) to enhance the adsorption efficiency. The maximum adsorption efficiency of 99.11 ± 0.23% was obtained using U. prolifera under the optimal conditions: pH: 5.4, adsorbent dosage: 200 mg, agitation speed: 160 rpm, and time: 75 min. Also, a prediction tool – artificial neural network (ANN) model was developed using the RSM experimental data. Eight neurons in the hidden layer yielded the best network topology (4-8-1) with a high correlation coefficient (RANN: 0.99219) and low mean squared error (MSEANN: 0.99219). Various perfor�mance parameters were compared between RSM and ANN models, which confirmed that the ANN model was better in predicting the response with a high coefficient of determination value (R2 ANN: 0.9844, R2 RSM: 0.9721)

Item Type: Article
Uncontrolled Keywords: Adsorption Macroalgae Ulva prolifera Response surface methodology Artificial Neural network
Subjects: Engineering > MIT Manipal > Biotechnology
Engineering > MIT Manipal > Chemical
Depositing User: MIT Library
Date Deposited: 27 Jul 2022 09:08
Last Modified: 27 Jul 2022 09:08
URI: http://eprints.manipal.edu/id/eprint/159036

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