Abstract：
:This paper demonstrates coupling of the artificial neural network (ANN) technique with the particle swarm optimization (PSO) method and compares the performance of ANN-PSO with response surface methodology (RSM) in prediction of the adsorption of methylene blue (MB) by a novel bio-superadsorbent. To this, a starch-based superadsorbent was synthesized using acrylic acid and acryl amid polymers and then catecholamine functional groups were combined onto the surface with oxidative polymerization of dopamine. The adsorption of MB was considered as a function of pH, dye concentration, and contact time. The best topology of the ANN was found to be 3-7-1, and prediction model of the adsorption capacity was demonstrated as a matrix of explicit equations. ANN-PSO is more accurate than RSM. The results revealed that the root-mean-square error, correlation coefficient, and normalized standard deviation for the ANN-PSO are 22.46, 0.99, and 16.83, respectively, while for RSM are 82.89, 0.98, and 65.41, respectively.
journal_name
Bioresour Technoljournal_title
Bioresource technologyauthors
Mahmoodi-Babolan N,Heydari A,Nematollahzadeh Adoi
10.1016/j.biortech.2019.122084subject
Has Abstractpub_date
2019-12-01 00:00:00pages
122084eissn
0960-8524issn
1873-2976pii
S0960-8524(19)31314-8journal_volume
294pub_type
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