Study the Different Artificial Neural Network Architecture in the Modeling of Flocculation of Chlorella sp. Microalgae with Ferric Chloride

Document Type : Research paper

Authors

1 Department of chemical ,Industry National university of Skills (NUS), Tehran,Iran

2 Department of Chemical Engineering, Amirkabir University of Technology

Abstract

Objective: Microalgae, as a group of photosynthetic microorganisms, hold great potential for the production of biofuels, particularly biodiesel. One of the main challenges in the industrial utilization of microalgae is the efficient separation of biomass from the culture medium. Among various proposed methods, flocculation has attracted considerable attention due to its cost-effectiveness and simplicity. This study aimed to investigate the flocculation performance of Chlorella sp. using ferric chloride (FeCl₃) as the flocculant and to model the process through artificial neural networks (ANNs).
Materials and methods: The Chlorella sp. strain was cultivated in Rudic medium, and flocculation experiments were conducted using FeCl₃ at various concentrations and environmental conditions. The experimental data were modeled and analyzed using three different ANN architectures: multilayer perceptron (MLP), radial basis function (RBF), and a hybrid structure combining optimized MLP networks.
Results: The standalone MLP and RBF models failed to provide accurate predictions of flocculation efficiency, especially under varying operational conditions. In contrast, the hybrid MLP architecture demonstrated a strong correlation with the experimental results. Statistical indicators such as the coefficient of determination (R²) and mean squared error (MSE) revealed the superior performance of the hybrid model. This approach was able to reliably predict outcomes even in scenarios with nonlinear or unstable biomass behavior, highlighting its robustness and adaptability.
Conclusions: The ensemble MLP-based ANN exhibited high potential in modeling complex biological processes such as microalgal flocculation. Its flexibility in handling diverse experimental data and learning intricate patterns makes it an ideal candidate for forecasting the efficiency of environmentally and industrially significant processes. These findings suggest that incorporating ensemble neural models could serve as a powerful tool in the design, control, and optimization of biomass harvesting and biofuel production systems.

Keywords

References

 
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Journal of Separation Science and Engineering
Volume 17, Issue 2
December 2025
Pages 90-108

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