relation: https://khub.utp.edu.my/scholars/11185/
title: Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
creator: Teng, S.Y.
creator: Loy, A.C.M.
creator: Leong, W.D.
creator: How, B.S.
creator: Chin, B.L.F.
creator: Máša, V.
description: 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
publisher: Elsevier Ltd
date: 2019
type: Article
type: PeerReviewed
identifier:   Teng, S.Y. and Loy, A.C.M. and Leong, W.D. and How, B.S. and Chin, B.L.F. and Máša, V.  (2019) Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization.  Bioresource Technology, 292.   ISSN 09608524     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070868058&doi=10.1016%2fj.biortech.2019.121971&partnerID=40&md5=9643b0ae124e6ec9ef2db6af29cc2e01
relation: 10.1016/j.biortech.2019.121971
identifier: 10.1016/j.biortech.2019.121971