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.
Microalgae, a group of photosynthetic microorganisms, are a promising feedstock for biodiesel production, but their biomass retrieval is a challenging task.Flocculation is a feasible method for dewatering and harvesting microalgae biomass. In the current study, the effect of alum flocculation on Chlorella vulgaris biomass retrieval has been studied. Alum structural changes with pH were led to a full factorial design to address the effect of this chemical structure changes at different pH values. It is observed that the best flocculation efficiency could be achieved in the natural pH value of C. vulgaris growth medium (8.2) with less than 0.5 g/L flocculant addition, which would lead to the flocculation efficiency of more than 90%. An ensemble architecture of neural networks successfully employed for flocculation modeling.
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