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Artificial neural networks for bio-based chemical production or biorefining: A review

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  • Pomeroy, Brett
  • Grilc, Miha
  • Likozar, Blaž

Abstract

Machine learning through artificial neural networks have emerged as vital tools to predict chemical behavior for many of the most recognized biomass valorization processes relevant to biorefineries for the purpose of optimization of desired products and reaction conditions. Until recently, these neural network methodologies have successfully been utilized in the petroleum industry where much more extensive databases are available for effective algorithm training. These systems provide compelling advantages for pattern recognition when interpreting the influence of ever-changing feedstock compositions for complex biomass conversion processes as they do not require any a prior knowledge of reaction mechanisms or thermodynamic phenomena. This has been revealed to be tremendously beneficial for real-time, dynamic control applications of biochemical processes for rapid parameter monitoring and regulation such as during fermentation or anaerobic digestion. This review aims to present and evaluate studies that have attempted to apply these neural network strategies to various aspects of biorefining and how these models address the common challenges that occur when relying on conventional mechanistic modelling approaches to estimate sophisticated, non-linear systems. Comparisons are then identified when implementing these artificial intelligence computing practices in traditional petroleum refineries where feedstock inconsistencies are not as paramount compared to biorefineries. Subsequently, the practicality of these neural networks is critically assessed and recommendations are presented on how to strengthen its applicability and predictability towards future bio-based chemical production. Mathematical models such as artificial neural networks will be an integral technology in the future bioeconomy for the realization of innovative biorefinery concepts as computational power continues to advance.

Suggested Citation

  • Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:rensus:v:153:y:2022:i:c:s1364032121010194
    DOI: 10.1016/j.rser.2021.111748
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    1. Safarian, Sahar & Ebrahimi Saryazdi, Seyed Mohammad & Unnthorsson, Runar & Richter, Christiaan, 2020. "Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant," Energy, Elsevier, vol. 213(C).
    2. Gueguim Kana, E.B. & Oloke, J.K. & Lateef, A. & Adesiyan, M.O., 2012. "Modeling and optimization of biogas production on saw dust and other co-substrates using Artificial Neural network and Genetic Algorithm," Renewable Energy, Elsevier, vol. 46(C), pages 276-281.
    3. Aghbashlo, Mortaza & Almasi, Fatemeh & Jafari, Ali & Nadian, Mohammad Hossein & Soltanian, Salman & Lam, Su Shiung & Tabatabaei, Meisam, 2021. "Describing biomass pyrolysis kinetics using a generic hybrid intelligent model: A critical stage in sustainable waste-oriented biorefineries," Renewable Energy, Elsevier, vol. 170(C), pages 81-91.
    4. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    5. Alexandre Boriouchkine & Sirkka-Liisa Jämsä-Jounela, 2016. "Simplification of a Mechanistic Model of Biomass Combustion for On-Line Computations," Energies, MDPI, vol. 9(9), pages 1-25, September.
    6. Maity, Sunil K., 2015. "Opportunities, recent trends and challenges of integrated biorefinery: Part I," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1427-1445.
    7. Muhammad Amin Durrani & Iftikhar Ahmad & Manabu Kano & Shinji Hasebe, 2018. "An Artificial Intelligence Method for Energy Efficient Operation of Crude Distillation Units under Uncertain Feed Composition," Energies, MDPI, vol. 11(11), pages 1-12, November.
    8. Anthony M. Zador, 2019. "A critique of pure learning and what artificial neural networks can learn from animal brains," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    9. Kirtika Kohli & Ravindra Prajapati & Brajendra K. Sharma, 2019. "Bio-Based Chemicals from Renewable Biomass for Integrated Biorefineries," Energies, MDPI, vol. 12(2), pages 1-40, January.
    10. Muhammad Ahsan Zamee & Dongjun Won, 2020. "Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction," Energies, MDPI, vol. 13(23), pages 1-29, December.
    11. Abu Qdais, H. & Bani Hani, K. & Shatnawi, N., 2010. "Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm," Resources, Conservation & Recycling, Elsevier, vol. 54(6), pages 359-363.
    12. Maity, Sunil K., 2015. "Opportunities, recent trends and challenges of integrated biorefinery: Part II," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1446-1466.
    13. Grahovac, Jovana & Jokić, Aleksandar & Dodić, Jelena & Vučurović, Damjan & Dodić, Siniša, 2016. "Modelling and prediction of bioethanol production from intermediates and byproduct of sugar beet processing using neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 953-958.
    14. Dach, J. & Koszela, K. & Boniecki, P. & Zaborowicz, M. & Lewicki, A. & Czekała, W. & Skwarcz, J. & Qiao, Wei & Piekarska-Boniecka, H. & Białobrzewski, I., 2016. "The use of neural modelling to estimate the methane production from slurry fermentation processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 603-610.
    15. Sakiewicz, P. & Piotrowski, K. & Ober, J. & Karwot, J., 2020. "Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: Effect of plant operating parameters on process intensification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    16. Lerkkasemsan, Nuttapol, 2017. "Fuzzy logic-based predictive model for biomass pyrolysis," Applied Energy, Elsevier, vol. 185(P2), pages 1019-1030.
    17. Mohammed I. Jahirul & Richard J. Brown & Wijitha Senadeera & Ian M. O'Hara & Zoran D. Ristovski, 2013. "The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks," Energies, MDPI, vol. 6(8), pages 1-43, July.
    18. Puig-Arnavat, Maria & Bruno, Joan Carles & Coronas, Alberto, 2010. "Review and analysis of biomass gasification models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2841-2851, December.
    19. Aghbashlo, Mortaza & Hosseinpour, Soleiman & Tabatabaei, Meisam & Dadak, Ali, 2017. "Fuzzy modeling and optimization of the synthesis of biodiesel from waste cooking oil (WCO) by a low power, high frequency piezo-ultrasonic reactor," Energy, Elsevier, vol. 132(C), pages 65-78.
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