Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization. (November 2019) - Teng, Sin Yong
Graphical abstract: Highlights: Thermal behavior of C. vulgaris were analyzed. Effects of the presence of limestone and HZSM-5 zeolite catalysts were investigated. Novel PDSE neuro-evolution algorithm was proposed for neural architecture search. Neuro-evolution achieved RMSE of 0.005–0.007, considered state-of-art for TGA studies. Simulated Annealing was used in optimization of operating conditions of the pyrolysis. 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.