title: Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.
abstract: Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.
authors.affiliation: a Department of Chemical Engineering , Amirkabir University of Technology , Tehran , Iran.
authors.forename: Alireza Moosavi
authors.initials: AM
authors.lastname: Zenooz
authors.affiliation: a Department of Chemical Engineering , Amirkabir University of Technology , Tehran , Iran.
authors.forename: Farzin Zokaee
authors.initials: FZ
authors.lastname: Ashtiani
authors.affiliation: a Department of Chemical Engineering , Amirkabir University of Technology , Tehran , Iran.|b Algaebiotech B.V., Rijnkade 17a Weesp , The Netherlands.
authors.forename: Reza
authors.initials: R
authors.lastname: Ranjbar
authors.affiliation: a Department of Chemical Engineering , Amirkabir University of Technology , Tehran , Iran.
authors.forename: Fatemeh
authors.initials: F
authors.lastname: Nikbakht
authors.affiliation: a Department of Chemical Engineering , Amirkabir University of Technology , Tehran , Iran.
authors.forename: Oberon
authors.initials: O
authors.lastname: Bolouri
mesh_terms: D056804:Biofuels; D002708:Chlorella / Q000737:chemistry / Q000254:growth & development* / Q000302:isolation & purification*; D002712:Chlorides / Q000737:chemistry; D003198:Computer Simulation; D005290:Ferric Compounds / Q000737:chemistry; D005427:Flocculation; D058086:Microalgae / Q000737:chemistry / Q000254:growth & development / Q000302:isolation & purification; D008954:Models, Biological; D008956:Models, Chemical; D016571:Neural Networks, Computer
chemical_list: D056804:Biofuels; D002712:Chlorides; D005290:Ferric Compounds; C024555:ferric chloride
keywords: Chlorella sp.; ensemble network; ferric chloride; flocculation; modeling; neural network