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

Teng, Sin Yong and Loy, Adrian Chun Minh and Leong, Wei Dong and How, Bing Shen and Chin, Bridgid Lai Fui and Máša, Vítězslav (2019): Catalytic thermal degradation of Chlorella Vulgaris: Evolving deep neural networks for optimization. Published in: Bioresource Technology , Vol. 292, No. 121971 (19 November 2019)

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Microalgae; Thermogravimetric analysis; Artificial neuron network; Particle swarm optimization; Simulated Annealing
• Subjects: C - Mathematical and Quantitative Methods > C0 - General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs; C - Mathematical and Quantitative Methods > C9 - Design of Experiments; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2 - Renewable Resources and Conservation; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q3 - Nonrenewable Resources and Conservation; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics

References

Abbas, T., Awais, M.M., Lockwood, F.C., 2003. An artificial intelligence treatment of devolatilization for pulverized coal and biomass in co-fired flames. Combust Flame 132(3), 305-318. doi.org/10.1016/S0010-2180(02)00482-0

Adenle, A.A., Haslam, G.E., Lee, L., 2013. Global assessment of research and development for algae biofuel production and its potential role for sustainable development in developing countries. Energ Policy 61, 182-195. doi.org/10.1016/j.enpol.2013.05.088

Alexander, D.L., Tropsha, A. and Winkler, D.A., 2015. Beware of R 2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 55(7), 1316-1322. doi.org/10.1021/acs.jcim.5b00206

Azizi, K., Moraveji, M.K., Najafabadi, H.A., 2017. Characteristics and kinetics study of simultaneous pyrolysis of microalgae Chlorella vulgaris, wood and polypropylene through TGA. Bioresource Technol 243, 481-491. doi.org/10.1016/j.biortech.2017.06.155

Bach, Q.V., Chen, W.H., 2017. Pyrolysis characteristics and kinetics of microalgae via thermogravimetric analysis (TGA): A state-of-the-art review. Bioresource Technol 246, 88100. doi.org/10.1016/j.biortech.2017.06.087

Barnard, J.A., Hughes, H.W.D., 1960. The pyrolysis of ethanol. T Faraday Soc 56, 55-63. 7. Buyukada, M., 2016. Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation. Bioresource Technol 216, 280-286. doi.org/10.1016/j.biortech.2016.05.091

Carlson, T.R., Jae, J., Lin, Y.-C., Tompsett, G.A., Huber, G.W., 2010. Catalytic fast pyrolysis of glucose with HZSM-5: The combined homogeneous and heterogeneous reactions. J Catal 270(1), 110-124. doi.org/10.1016/j.jcat.2009.12.013 Catalytic Thermal Degradation of Chlorella Vulgaris: Evolving Deep Neural Networks for Optimization 2019

Chan, Y.H., Cheah, K.W., How, B.S., Loy, A.C.M., Shahbaz, M., Singh, H.K.G., Yusuf, N.R., Ahmad Shuhaili, A.F., Yusup, S., Ghani, W.A.W.A.K., Rambli, J., Kansha, Y., Lam, H.L., Hong, B.H., Ngan, S.L., 2019. An overview of biomass thermochemical conversion technologies in Malaysia. Sci Total Environ 680, 105-123. doi.org/10.1016/j.scitotenv.2019.04.211

Chen, X., Li, S., Liu, Z., Chen, Y., Yang, H., Wang, X., Che, Q., Chen, W., Chen, H., 2019. Pyrolysis characteristics of lignocellulosic biomass components in the presence of CaO. Bioresource Technol 287, 121493. doi.org/10.1016/j.biortech.2019.121493

Conesa, J.A., Caballero, J.A., Reyes-Labarta, J.A., 2004. Artificial neural network for modelling thermal decompositions. J Anal Appl Pyrol 71(1), 343-352. doi.org/10.1016/S01652370(03)00093-7

Costa, J.A.V., Morais, M.G., 2011. The role of biochemical engineering in the production of biofuels from microalgae, Bioresource Technol 102(1), 2-9. doi.org/10.1016/j.biortech.2010.06.014

Ding, S., Li, H., Su, C., Yu, J., Jin, F., 2011. Evolutionary artificial neural networks: a review. Artif Intell Rev 39(3), 251-260. doi.org/10.1007/s10462-011-9270-6

Fazilat, H., Akhlaghi, S., Shiri, M.E., Sharif, A., 2012. Predicting thermal degradation kinetics of nylon6/feather keratin blends using artificial intelligence techniques. Polymer 53(11), 22552264. doi.org/10.1016/j.polymer.2012.03.053

Figueira, C.E., Moreira Jr, P.F., Giudici, R., 2015. Thermogravimetric analysis of the gasification of microalgae Chlorella vulgaris. Bioresource Technol 198, 717-724. doi.org/10.1016/j.biortech.2015.09.059

Fister Jr, I., Yang, X.S., Fister, I., Brest, J. and Fister, D., 2013. A brief review of natureinspired algorithms for optimization. arXiv preprint arXiv:1307.4186.

Floreano, D., Dürr, P., Mattiussi, C., 2008. Neuroevolution: from architectures to learning. Evol Intell 1(1), 47-62. doi.org/10.1007/s12065-007-0002-4 18. Gai, C., Zhang, Y., Chen, W.-T., Zhang, P., Dong, Y., 2013. Thermogravimetric and kinetic analysis of thermal decomposition characteristics of low-lipid microalgae. Bioresource Technol 150, 139-148. doi.org/10.1016/j.biortech.2013.09.137

Gan, D.K.W., Loy, A.C.M., Chin, B.L.F., Yusup, S., Unrean, P., Rianawati, E., Acda, M.N., 2018. Kinetics and thermodynamic analysis in one-pot pyrolysis of rice hull using renewable calcium oxide based catalysts. Bioresource Technol 265, 180-190. doi.org/10.1016/j.biortech.2018.06.003

Goffe, W.L., Ferrier, G.D., Rogers, J., 1994. Global optimization of statistical functions with simulated annealing. J Econometrics 60(1-2), 65-99. doi.org/10.1016/0304-4076(94)90038-8 21. International Energy Agency (IEA), 2019. Global energy demand rose by 2.3% in 2018, its fastest pace in the last decade. https://www.iea.org/newsroom/news/2019/march/globalenergy-demand-rose-by-23-in-2018-its-fastest-pace-in-the-last-decade.html (accessed June 17, 2019).

Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W.M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K. and Fernando, C., 2017. Population based training of neural networks. arXiv preprint arXiv:1711.09846.

Kim, S.-S., Ly, H.V., Choi, G.-H., Kim, J., Woo, H.C., 2012. Pyrolysis characteristics and kinetics of the alga Saccharina japonica. Bioresource Technol 123, 445-451. doi.org/10.1016/j.biortech.2012.07.097

Kingma, D.P., Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 25. Mata, T.M., Martins, A.A., Caetano, N.S., 2010. Microalgae for biodiesel production and other applications: A review. Renew Sust Energ Rev 14(1): 217-232. doi.org/10.1016/j.rser.2009.07.020

Mayol, A., Maningo, J., Chua-Unsu, A., Felix, C., Rico, P., Chua, G., Manalili, E., Fernandez, D., Cuello, J., Bandala, A., Ubando, A., Madrazo, C., Dadios, E. and Culaba, A., 2018. Application of Artificial Neural Networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). IEEE, Baguio City, Philippines. doi.org/ 10.1109/HNICEM.2018.8666376

Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E., 1953. Equation of state calculations by fast computing machines. J Chem Phys 21(6), 1087-1092. doi.org/10.1063/1.1699114

Mettler, M.S., Paulsen, A.D., Vlachos, D.G., Dauenhauer, P.J., 2014. Tuning cellulose pyrolysis chemistry: selective decarbonylation via catalyst-impregnated pyrolysis. Catal Sci Technol 4, 3822-3825. doi.org/10.1039/C4CY00676C

Mhaskar, H.N., Poggio, T., 2016. Deep vs. shallow networks: An approximation theory perspective. Analy Appl 14(6), 829-848. doi.org/10.1142/S0219530516400042

Milano, J., Ong, H.C., Masjuki, H.H., Chong, W.T., Lam, M.K., Loh, P.K., Vellayan, V., 2016. Microalgae biofuels as an alternative to fossil fuel for power generation. Renew Sust Energ Rev 58, 180-197. doi.org/10.1016/j.rser.2015.12.150

Mohr, A., Raman, S., 2013. Lessons from first generation biofuels and implications for the sustainability appraisal of second generation biofuels. Energ Policy 63, 114-122. doi./10.1016/j.enpol.2013.08.033

Naqvi, S.R., Tariq, R., Hameed, Z., Ali, I., Taqvi, S.A., Naqvi, M., Niazi, M.B.K., Noor, T., Farooq, W., 2018. Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks. Fuel 233, 529-538. doi.org/10.1016/j.fuel.2018.06.089

Organization of the Petroleum Exporting Countries (OPEC), 2019. OPEC: energy, climate change and sustainable development. https://www.opec.org/opec_web/static_files_project/media/downloads/publications/OB0420 19.pdf (accessed June 19, 2019).

Raymundo, L.M., Mullen, C.A., Strahan, G.D., Boateng, A.A., Trierweiler, J.O., 2019. Deoxygenation of Biomass Pyrolysis Vapors via in Situ and ex Situ Thermal and Biochar Promoted Upgrading. Energ Fuels 33(3), 2197-2207. doi.org/10.1021/acs.energyfuels.8b03281

Salimans, T., Ho, J., Chen, X., Sidor, S. and Sutskever, I., 2017. Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864.

Shi, Y., Eberhart, R., 1998. A modified particle swarm optimizer. In 1998 IEEE international conference on evolutionary computation proceedings. In 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360). IEEE, Anchorage, USA. doi.org/10.1109/ICEC.1998.699146

Stanley, K.O., Clune, J., Lehman, J., Miikkulainen, R., 2019. Designing neural networks through neuroevolution. Nat Mach Intell 1(1), 24-35. doi.org/10.1038/s42256-018-0006-z

Stanley, K.O., Miikkulainen, R., 2002. Evolving neural networks through augmenting topologies. Evol Comput 10(2), 99-127. doi.org/10.1162/106365602320169811

Sun, J., Xiong, X., Wang, M., Du, H., Li, J., Zhou, D., Zuo, J., 2019. Microalgae biodiesel production in China: A preliminary economic analysis. Renew Sust Energ Rev 104, 296-306. doi.org/10.1016/j.rser.2019.01.021

Tan, K.C., Chiam, S.C., Mamun, A.A., Goh, C.K., 2009. Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Eur J Oper Res 197(2), 701-713. doi.org/10.1016/j.ejor.2008.07.025

Teng, S.Y., 2019. tsyet12/EvoOpt: EvoOpt v0.12 pre-release. doi.org/10.5281/zenodo.3241951

Vitolo, S., Bresci, B., Seggiani, M., Gallo, M.G., 2001. Catalytic upgrading of pyrolytic oils over HZSM-5 zeolite: behaviour of the catalyst when used in repeated upgrading–regenerating cycles. Fuel 80(1), 17-26. doi.org/10.1016/S0016-2361(00)00063-6

Xie, C., Liu, J., Zhang, X., Xie, W., Sun, J., Chang, K., Kuo, J., Xie, W., Liu, C., Sun, S., Buyukada, M., 2018. Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks. Appl Energ 212, 786-795. doi.org/10.1016/j.apenergy.2017.12.084

Yang, H., Norinaga, K., Li, J., Zhu, W., Wang, H., 2018. Effects of HZSM-5 on volatile products obtained from the fast pyrolysis of lignin and model compounds. Fuel Process Technol 181, 207-214. doi.org/10.1016/j.fuproc.2018.09.022

Yıldız, Z., Uzun, H., Ceylan, S., Topcu, Y., 2016. Application of artificial neural networks to co-combustion of hazelnut husk–lignite coal blends. Bioresource Technol 200, 42-47. doi.org/10.1016/j.biortech.2015.09.114

Zainan, N.H., Srivatsa, S.C., Li, F., Bhattacharya, S., 2018. Quality of bio-oil from catalytic pyrolysis of microalgae Chlorella vulgaris. Fuel 223, 12-19. doi.org/10.1016/j.fuel.2018.02.166

Zhang, X., Li, C., Tian, A., Guo, Q., Huang, K., 2019. Influence of CaO and HZSM-5 on oxygen migration in Chlorella vulgaris polysaccharide pyrolysis. Carbon Resources Conversion, 2(2), 111-116. doi.org/10.1016/j.crcon.2019.05.00