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

Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
type: AcademicArticle
type: Article
abstract: 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. © 2019 Elsevier Ltd
date: 2019
volume: 292
is part of: Bioresource Technology
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Elsevier Ltd
type: Organization

Bioresource Technology
type: Collection
issn: 09608524

V. MÃ¡Å¡a
type: Person
given name: V.
family name: MÃ¡Å¡a

W.D. Leong
type: Person
given name: W.D.
family name: Leong

A.C.M. Loy
type: Person
given name: A.C.M.
family name: Loy

S.Y. Teng
type: Person
given name: S.Y.
family name: Teng

B.S. How
type: Person
given name: B.S.
family name: How

B.L.F. Chin
type: Person
given name: B.L.F.
family name: Chin

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