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The use of Artificial Neural Network models for CO2 capture plants

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  • Sipöcz, Nikolett
  • Tobiesen, Finn Andrew
  • Assadi, Mohsen

Abstract

Artificial Neural Networks (ANN) are multifaceted tools that can be used to model and predict various complex and highly non-linear processes. This paper presents the development and validation of an ANN model of a CO2 capture plant. An evaluation of the concept is made of the usefulness of the ANN model as well as a discussion of its feasibility for further integration into a conventional heat and mass balance programme. It is shown that the trained ANN model can reproduce the results of a rigorous process simulator in fraction of the simulation time. A multilayer feed-forward form of Artificial Neural Network was used to capture and model the non-linear relationship between inputs and outputs of the CO2 capture process. The data used for training and validation of the ANN were obtained using the process simulator CO2SIM. The ANN model was trained by performing fully automatic batch simulations using CO2SIM over the entire range of actual operation for an amine based absorption plant. The trained model was then used for finding the optimum operation for the example plant with respect to lowest possible specific steam duty and maximum CO2 capture rate. Two different algorithms have been used and compared for the training of the ANN and a sensitivity analysis was carried out to find the minimum number of input parameters needed while maintaining sufficient accuracy of the model. The reproducibility shows error less than 0.2% for the closed loop absorber/desorber plant. The results of this study show that trained ANN models are very useful for fast simulation of complex steady state process with high reproducibility of the rigorous model.

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  • Sipöcz, Nikolett & Tobiesen, Finn Andrew & Assadi, Mohsen, 2011. "The use of Artificial Neural Network models for CO2 capture plants," Applied Energy, Elsevier, vol. 88(7), pages 2368-2376, July.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:7:p:2368-2376
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    1. Fast, M. & Assadi, M. & De, S., 2009. "Development and multi-utility of an ANN model for an industrial gas turbine," Applied Energy, Elsevier, vol. 86(1), pages 9-17, January.
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    2. Talebian-Kiakalaieh, Amin & Amin, Nor Aishah Saidina & Zarei, Alireza & Noshadi, Iman, 2013. "Transesterification of waste cooking oil by heteropoly acid (HPA) catalyst: Optimization and kinetic model," Applied Energy, Elsevier, vol. 102(C), pages 283-292.
    3. Wu, Xiao & Wang, Meihong & Liao, Peizhi & Shen, Jiong & Li, Yiguo, 2020. "Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation," Applied Energy, Elsevier, vol. 257(C).
    4. Errico, Massimiliano & Madeddu, Claudio & Pinna, Daniele & Baratti, Roberto, 2016. "Model calibration for the carbon dioxide-amine absorption system," Applied Energy, Elsevier, vol. 183(C), pages 958-968.
    5. Mores, Patricia & Scenna, Nicolás & Mussati, Sergio, 2012. "CO2 capture using monoethanolamine (MEA) aqueous solution: Modeling and optimization of the solvent regeneration and CO2 desorption process," Energy, Elsevier, vol. 45(1), pages 1042-1058.
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    7. Wu, Xiao & Shen, Jiong & Wang, Meihong & Lee, Kwang Y., 2020. "Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization," Energy, Elsevier, vol. 196(C).
    8. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
    9. Goto, Kazuya & Yogo, Katsunori & Higashii, Takayuki, 2013. "A review of efficiency penalty in a coal-fired power plant with post-combustion CO2 capture," Applied Energy, Elsevier, vol. 111(C), pages 710-720.
    10. Morgan, Joshua C. & Chinen, Anderson Soares & Anderson-Cook, Christine & Tong, Charles & Carroll, John & Saha, Chiranjib & Omell, Benjamin & Bhattacharyya, Debangsu & Matuszewski, Michael & Bhat, K. S, 2020. "Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process," Applied Energy, Elsevier, vol. 262(C).
    11. Nabipour, Narjes & Daneshfar, Reza & Rezvanjou, Omid & Mohammadi-Khanaposhtani, Mohammad & Baghban, Alireza & Xiong, Qingang & Li, Larry K.B. & Habibzadeh, Sajjad & Doranehgard, Mohammad Hossein, 2020. "Estimating biofuel density via a soft computing approach based on intermolecular interactions," Renewable Energy, Elsevier, vol. 152(C), pages 1086-1098.
    12. Dong, Ruifeng & Lu, Hongfang & Yu, Yunsong & Zhang, Zaoxiao, 2012. "A feasible process for simultaneous removal of CO2, SO2 and NOx in the cement industry by NH3 scrubbing," Applied Energy, Elsevier, vol. 97(C), pages 185-191.
    13. Wu, Xiao & Wang, Meihong & Shen, Jiong & Li, Yiguo & Lawal, Adekola & Lee, Kwang Y., 2019. "Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls," Applied Energy, Elsevier, vol. 238(C), pages 495-515.
    14. Kim, Youngmin & Jang, Hochang & Kim, Junggyun & Lee, Jeonghwan, 2017. "Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network," Applied Energy, Elsevier, vol. 185(P1), pages 916-928.
    15. Chen, Wei-Hsin & Chen, Shu-Mi & Hung, Chen-I, 2013. "Carbon dioxide capture by single droplet using Selexol, Rectisol and water as absorbents: A theoretical approach," Applied Energy, Elsevier, vol. 111(C), pages 731-741.

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