期刊
BIORESOURCE TECHNOLOGY
卷 292, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2019.121971
关键词
Microalgae; Thermogravimetric analysis; Artificial neuron network; Particle swarm optimization; Simulated Annealing
资金
- Ministry of Education, Youth and Sports of the Czech Republic under OP RDE [CZ.02.1.01/0.0/0.0/16_026/0008413]
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 degrees C, heating rate of 5.0 degrees C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% of Chlorella vulgaris conversion.
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