Detail publikačního výsledku

Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization

TENG, S., LOY, A., LEONG, W., HOW, B., CHIN, B., MÁŠA, V.

Originální název

Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization

Anglický název

Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization

Druh

Článek WoS

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

Microalgae, Thermogravimetric analysis, Artificial neuron network, Particle swarm optimization, simulated annealing

Klíčová slova v angličtině

Microalgae, Thermogravimetric analysis, Artificial neuron network, Particle swarm optimization, simulated annealing

Autoři

TENG, S., LOY, A., LEONG, W., HOW, B., CHIN, B., MÁŠA, V.

Rok RIV

2020

Vydáno

01.11.2019

Nakladatel

Elsevier

Místo

Oxford, England

ISSN

0960-8524

Periodikum

BIORESOURCE TECHNOLOGY

Svazek

292

Číslo

121971

Stát

Nizozemsko

Strany od

1

Strany do

9

Strany počet

292

URL

https://www.sciencedirect.com/science/article/pii/S0960852419312015

BibTex

@article{BUT160578,
  author="Sin Yong {Teng} and Vítězslav {Máša}",
  title="Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization",
  journal="BIORESOURCE TECHNOLOGY",
  year="2019",
  volume="292",
  number="121971",
  pages="1--9",
  doi="10.1016/j.biortech.2019.121971",
  issn="0960-8524",
  url="https://www.sciencedirect.com/science/article/pii/S0960852419312015"
}

Dokumenty

160578 paper
160578-detailni_zduvodneni_en