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Updated: Jan 20, 2026

Author Spotlight: Optimized Transformation Protocol for Chlorella vulgaris Using Agrobacterium tumefaciens
Published on: October 27, 2023
Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
Sin Yong Teng1, Adrian Chun Minh Loy2, Wei Dong Leong3
1Brno University of Technology, Institute of Process Engineering & NETME Centre, Technicka 2896/2, 616 69 Brno, Czech Republic.
Abstract:
The aim of this study is to identify the optimum thermal conversion of Chlorella vulgaris with neuro-evolutionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed to model the Thermogravimetric analysis (TGA) data of catalytic thermal degradation of Chlorella vulgaris. Results showed that the proposed method can generate predictions which are more accurate compared to other conventional approaches (>90% lower in Root Mean Square Error (RMSE) and Mean Bias Error (MBE)). In addition, Simulated Annealing is proposed to determine the optimal operating conditions for microalgae conversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of 900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% of Chlorella vulgaris conversion.
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