TY - JOUR
T1 - Catalytic thermal degradation of Chlorella vulgaris
T2 - evolving deep neural networks for optimization
AU - Teng, Sin Yong
AU - Loy, Adrian Chun Minh
AU - Leong, Wei Dong
AU - How, Bing Shen
AU - Chin, Bridgid Lai Fui
AU - Máša, Vítězslav
N1 - Funding Information:
The authors would like to acknowledge financial support from the Ministry of Education, Youth and Sports of the Czech Republic under OP RDE [grant number CZ.02.1.01/0.0/0.0/16_026/0008413 ] “Strategic Partnership for Environmental Technologies and Energy Production”. We would also like to acknowledge Melissa and her team for providing their experimental data to the authors for this study.
Publisher Copyright:
© 2019 Elsevier Ltd
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - Artificial neuron network
KW - Microalgae
KW - Particle swarm optimization
KW - Simulated Annealing
KW - Thermogravimetric analysis
UR - http://www.scopus.com/inward/record.url?scp=85070868058&partnerID=8YFLogxK
U2 - 10.1016/j.biortech.2019.121971
DO - 10.1016/j.biortech.2019.121971
M3 - Article
C2 - 31445240
AN - SCOPUS:85070868058
VL - 292
JO - Bioresource Technology
JF - Bioresource Technology
SN - 0960-8524
M1 - 121971
ER -
