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.

Original Title

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

Type

journal article in Web of Science

Language

English

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.

Keywords

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

Authors

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

Released

1. 11. 2019

Publisher

Elsevier

Location

Oxford, England

ISBN

0960-8524

Periodical

BIORESOURCE TECHNOLOGY

Year of study

292

Number

121971

State

Kingdom of the Netherlands

Pages from

1

Pages to

9

Pages count

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"
}