Detail publikace
Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
TENG, S. LOY, A. LEONG, W. HOW, B. CHIN, B. MÁŠA, V.
Anglický název
Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
en
Originální abstrakt
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.
Anglický abstrakt
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.
Klíčová slova anglicky
Microalgae, Thermogravimetric analysis, Artificial neuron network, Particle swarm optimization, simulated annealing
Vydáno
01.11.2019
Nakladatel
Elsevier
Místo
Oxford, England
ISSN
0960-8524
Ročník
292
Číslo
121971
Strany od–do
1–9
Počet stran
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"
}