eprintid: 11185
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/11/85
datestamp: 2023-11-10 03:25:43
lastmod: 2023-11-10 03:25:43
status_changed: 2023-11-10 01:14:39
type: article
metadata_visibility: show
creators_name: Teng, S.Y.
creators_name: Loy, A.C.M.
creators_name: Leong, W.D.
creators_name: How, B.S.
creators_name: Chin, B.L.F.
creators_name: Máša, V.
title: Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization
ispublished: pub
keywords: Algae; Deep neural networks; Mean square error; Microorganisms; Particle swarm optimization (PSO); Simulated annealing; Zeolites, Artificial neuron networks; Chlorella vulgaris; Conventional approach; Evolutionary approach; Micro-algae; Optimal operating conditions; Reaction temperature; Root mean square errors, Thermogravimetric analysis, artificial neural network; catalysis; environmental degradation; microalga; optimization; simulated annealing; thermal decomposition; thermogravimetry, article; catalyst; Chlorella vulgaris; heating; microalga; nerve cell; nonhuman; prediction; reaction temperature; simulation; thermogravimetry; artificial neural network; catalysis; temperature, Chlorella vulgaris, Catalysis; Chlorella vulgaris; Microalgae; Neural Networks (Computer); Temperature
note: cited By 38
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
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070868058&doi=10.1016%2fj.biortech.2019.121971&partnerID=40&md5=9643b0ae124e6ec9ef2db6af29cc2e01
id_number: 10.1016/j.biortech.2019.121971
full_text_status: none
publication: Bioresource Technology
volume: 292
refereed: TRUE
issn: 09608524
citation:   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