Catalytic thermal degradation of Chlorella vulgaris: evolving deep neural networks for optimization

Sin Yong Teng, Adrian Chun Minh Loy, Wei Dong Leong, Bing Shen How, Bridgid Lai Fui Chin, Vítězslav Máša

Research output: Contribution to journalArticleResearchpeer-review

39 Citations (Scopus)


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.

Original languageEnglish
Article number121971
JournalBioresource Technology
Publication statusPublished - Nov 2019
Externally publishedYes


  • Artificial neuron network
  • Microalgae
  • Particle swarm optimization
  • Simulated Annealing
  • Thermogravimetric analysis

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