Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.

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.

Experimental studies and statistical analysis of membrane fouling behavior and performance in microfiltration of microalgae by a gas sparging assisted process.

Response surface methodology (RSM) and central composite design (CCD) were applied for modeling and optimization of cross-flow microfiltration of Chlorella sp. suspension. The effects of operating conditions, namely transmembrane pressure (TMP), feed flow rate (Qf) and optical density of feed suspension (ODf), on the permeate flux and their interactions were determined. Analysis of variance (ANOVA) was performed to test the significance of response surface model. The effect of gas sparging technique and different gas-liquid two phase flow regimes on the permeate flux was also investigated. Maximum flux enhancement was 61% and 15% for Chlorella sp. with optical densities of 1.0 and 3.0, respectively. These results indicated that gas sparging technique was more efficient in low concentration microalgae microfiltration in which up to 60% enhancement was achieved in slug flow pattern. Additionally, variations in the transmission of exopolysaccharides (EPS) and its effects on the fouling phenomenon were evaluated.