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Energy efficiency optimisation for distillation column using artificial neural network models

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  • Osuolale, Funmilayo N.
  • Zhang, Jie

Abstract

This paper presents a neural network based strategy for the modelling and optimisation of energy efficiency in distillation columns incorporating the second law of thermodynamics. Real-time optimisation of distillation columns based on mechanistic models is often infeasible due to the effort in model development and the large computation effort associated with mechanistic model computation. This issue can be addressed by using neural network models which can be quickly developed from process operation data. The computation time in neural network model evaluation is very short making them ideal for real-time optimisation. Bootstrap aggregated neural networks are used in this study for enhanced model accuracy and reliability. Aspen HYSYS is used for the simulation of the distillation systems. Neural network models for exergy efficiency and product compositions are developed from simulated process operation data and are used to maximise exergy efficiency while satisfying products qualities constraints. Applications to binary systems of methanol-water and benzene-toluene separations culminate in a reduction of utility consumption of 8.2% and 28.2% respectively. Application to multi-component separation columns also demonstrate the effectiveness of the proposed method with a 32.4% improvement in the exergy efficiency.

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  • Osuolale, Funmilayo N. & Zhang, Jie, 2016. "Energy efficiency optimisation for distillation column using artificial neural network models," Energy, Elsevier, vol. 106(C), pages 562-578.
    • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:562-578
    DOI: 10.1016/j.energy.2016.03.051
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    References listed on IDEAS

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    1. Kiss, Anton A. & Flores Landaeta, Servando J. & Infante Ferreira, Carlos A., 2012. "Towards energy efficient distillation technologies – Making the right choice," Energy, Elsevier, vol. 47(1), pages 531-542.
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    3. Kazemi, Abolghasem & Mehrabani-Zeinabad, Arjomand & Beheshti, Masoud, 2017. "Development of a novel processing system for efficient sour water stripping," Energy, Elsevier, vol. 125(C), pages 449-458.
    4. Kazemi, Abolghasem & Mehrabani-Zeinabad, Arjomand & Beheshti, Masoud, 2018. "Recently developed heat pump assisted distillation configurations: A comparative study," Applied Energy, Elsevier, vol. 211(C), pages 1261-1281.
    5. Đozić, Damir J. & Gvozdenac Urošević, Branka D., 2019. "Application of artificial neural networks for testing long-term energy policy targets," Energy, Elsevier, vol. 174(C), pages 488-496.
    6. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    7. Zhang, Chuanfang & Peng, Kaixiang & Dong, Jie & Zhang, Xueyi & Yang, Kaixuan, 2023. "Exergy-related process monitoring for hot strip mill process based on improved support tensor data description," Energy, Elsevier, vol. 284(C).

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