Publication detail
Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
TENG, S. LOY, A. LEONG, W. HOW, B. CHIN, B. MÁŠA, V.
English title
Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
Type
journal article in Web of Science
Language
en
Original abstract
The aim of this study is to identify the optimum thermal conversion ofChlorella vulgariswith neuro-evolu-tionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed tomodel the Thermogravimetric analysis (TGA) data of catalytic thermal degradation ofChlorella vulgaris.Results showed that the proposed method can generate predictions which are more accurate compared toother 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 microalgaeconversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% ofChlorellavulgarisconversion.
English abstract
The aim of this study is to identify the optimum thermal conversion ofChlorella vulgariswith neuro-evolu-tionary approach. A Progressive Depth Swarm-Evolution (PDSE) neuro-evolutionary approach is proposed tomodel the Thermogravimetric analysis (TGA) data of catalytic thermal degradation ofChlorella vulgaris.Results showed that the proposed method can generate predictions which are more accurate compared toother 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 microalgaeconversion from multiple trained ANN. The predicted optimum conditions were reaction temperature of900.0 °C, heating rate of 5.0 °C/min with the presence of HZSM-5 zeolite catalyst to obtain 88.3% ofChlorellavulgarisconversion.
Keywords in English
Microalgae, Thermogravimetric analysis, Artificial neuron network, Particle swarm optimization, simulated annealing
Released
01.11.2019
Publisher
Elsevier
Location
Oxford, England
ISSN
0960-8524
Volume
292
Number
121971
Pages from–to
1–9
Pages count
292
BIBTEX
@article{BUT160578,
author="Sin Yong {Teng} and Vítězslav {Máša},
title="Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization",
year="2019",
volume="292",
number="121971",
month="November",
pages="1--9",
publisher="Elsevier",
address="Oxford, England",
issn="0960-8524"
}