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

Author

Listed:
  • Teng, Sin Yong
  • Loy, Adrian Chun Minh
  • Leong, Wei Dong
  • How, Bing Shen
  • Chin, Bridgid Lai Fui
  • Máša, Vítězslav

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.

Suggested Citation

  • Teng, Sin Yong & Loy, Adrian Chun Minh & Leong, Wei Dong & How, Bing Shen & Chin, Bridgid Lai Fui & Máša, Vítězslav, 2019. "Catalytic thermal degradation of Chlorella Vulgaris: Evolving deep neural networks for optimization," MPRA Paper 95772, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:95772
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    Cited by:

    1. Bong, Jang Tyng & Loy, Adrian Chun Minh & Chin, Bridgid Lai Fui & Lam, Man Kee & Tang, Daniel Kuok Ho & Lim, Huei Yeong & Chai, Yee Ho & Yusup, Suzana, 2020. "Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst," Energy, Elsevier, vol. 207(C).
    2. Chu, C. & Boré, A. & Liu, X.W. & Cui, J.C. & Wang, P. & Liu, X. & Chen, G.Y. & Liu, B. & Ma, W.C. & Lou, Z.Y. & Tao, Y. & Bary, A., 2022. "Modeling the impact of some independent parameters on the syngas characteristics during plasma gasification of municipal solid waste using artificial neural network and stepwise linear regression meth," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    3. Hakimian, Hanie & Pyo, Sumin & Kim, Young-Min & Jae, Jungho & Show, Pau Loke & Rhee, Gwang Hoon & Chen, Wei-Hsin & Park, Young-Kwon, 2022. "Increased aromatics production by co-feeding waste oil sludge to the catalytic pyrolysis of cellulose," Energy, Elsevier, vol. 239(PD).
    4. Lim, Juin Yau & Teng, Sin Yong & How, Bing Shen & Nam, KiJeon & Heo, SungKu & Máša, Vítězslav & Stehlík, Petr & Yoo, Chang Kyoo, 2022. "From microalgae to bioenergy: Identifying optimally integrated biorefinery pathways and harvest scheduling under uncertainties in predicted climate," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    5. Teng, Sin Yong & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav & Stehlík, Petr, 2021. "Debottlenecking cogeneration systems under process variations: Multi-dimensional bottleneck tree analysis with neural network ensemble," Energy, Elsevier, vol. 215(PB).

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    More about this item

    Keywords

    Microalgae; Thermogravimetric analysis; Artificial neuron network; Particle swarm optimization; Simulated Annealing;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

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