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Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant

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  • Aliyon, Kasra
  • Rajaee, Fatemeh
  • Ritvanen, Jouni

Abstract

Absorption-based carbon capture (ACC) is the most mature technology for abating CO2 among different post-combustion carbon capture technologies. ACC is, however, energy-intensive and requires large heating and cooling utility consumption, resulting in high operational costs. Reliable and fast estimation of these utility consumptions would be valuable for technical and financial feasibility assessment of different ACC process designs. The objective of this study is to develop an artificial intelligence (AI) based approach that predicts these utility consumptions of different ACC process designs. For prediction of specific reboiler duty, which is the most important energy consumption of the plant and the main source of operational costs in the ACC process, the error of the AI models is between 0.4% and 3.6% for different scenarios of utilizing either limited or extensive training data. Moreover, the contribution of each process parameter to energy consumption was identified using explainable artificial intelligence. The findings of this study facilitate the energy-efficient design of ACC plants and provide a prioritized list of parameters to adjust for reducing the energy consumption of an ACC process.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223012288
    DOI: 10.1016/j.energy.2023.127834
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    as
    1. Al-Hamed, Khaled H.M. & Dincer, Ibrahim, 2022. "Exergoeconomic analysis and optimization of a solar energy-based integrated system with oxy-combustion for combined power cycle and carbon capturing," Energy, Elsevier, vol. 250(C).
    2. Park, Sung Ho & Lee, Seung Jong & Lee, Jin Wook & Chun, Sung Nam & Lee, Jung Bin, 2015. "The quantitative evaluation of two-stage pre-combustion CO2 capture processes using the physical solvents with various design parameters," Energy, Elsevier, vol. 81(C), pages 47-55.
    3. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    4. Liu, Gengfeng & Zhang, Xiangwen & Liu, Zhiming, 2022. "State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm," Energy, Elsevier, vol. 259(C).
    5. 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.
    6. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    7. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    8. Shalaby, Abdelhamid & Elkamel, Ali & Douglas, Peter L. & Zhu, Qinqin & Zheng, Qipeng P., 2021. "A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit," Energy, Elsevier, vol. 215(PA).
    9. Li, Jinlong & Wang, ZhuoTeng & Zhang, Shuai & Shi, Xilin & Xu, Wenjie & Zhuang, Duanyang & Liu, Jia & Li, Qingdong & Chen, Yunmin, 2022. "Machine-learning-based capacity prediction and construction parameter optimization for energy storage salt caverns," Energy, Elsevier, vol. 254(PA).
    10. Golizadeh Akhlaghi, Yousef & Aslansefat, Koorosh & Zhao, Xudong & Sadati, Saba & Badiei, Ali & Xiao, Xin & Shittu, Samson & Fan, Yi & Ma, Xiaoli, 2021. "Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050," Applied Energy, Elsevier, vol. 281(C).
    11. Szega, Marcin & Żymełka, Piotr & Janda, Tomasz, 2022. "Improving the accuracy of electricity and heat production forecasting in a supervision computer system of a selected gas-fired CHP plant operation," Energy, Elsevier, vol. 239(PE).
    12. Lin, Mingqiang & Yan, Chenhao & Meng, Jinhao & Wang, Wei & Wu, Ji, 2022. "Lithium-ion batteries health prognosis via differential thermal capacity with simulated annealing and support vector regression," Energy, Elsevier, vol. 250(C).
    13. Wang, Erlei & Xia, Jiangying & Li, Jia & Sun, Xianke & Li, Hao, 2022. "Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm," Energy, Elsevier, vol. 261(PA).
    14. Chegari, Badr & Tabaa, Mohamed & Simeu, Emmanuel & Moutaouakkil, Fouad & Medromi, Hicham, 2022. "An optimal surrogate-model-based approach to support comfortable and nearly zero energy buildings design," Energy, Elsevier, vol. 248(C).
    15. Dai, Yeming & Wang, Yanxin & Leng, Mingming & Yang, Xinyu & Zhou, Qiong, 2022. "LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method," Energy, Elsevier, vol. 256(C).
    16. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    17. Spinti, Jennifer P. & Smith, Philip J. & Smith, Sean T., 2022. "Atikokan Digital Twin: Machine learning in a biomass energy system," Applied Energy, Elsevier, vol. 310(C).
    18. Bartela, Łukasz & Skorek-Osikowska, Anna & Kotowicz, Janusz, 2014. "Economic analysis of a supercritical coal-fired CHP plant integrated with an absorption carbon capture installation," Energy, Elsevier, vol. 64(C), pages 513-523.
    19. Rubén M. Montañés & Nina E. Flø & Lars O. Nord, 2017. "Dynamic Process Model Validation and Control of the Amine Plant at CO 2 Technology Centre Mongstad," Energies, MDPI, vol. 10(10), pages 1-36, October.
    20. Akinola, Toluleke E. & Bonilla Prado, Phebe L. & Wang, Meihong, 2022. "Experimental studies, molecular simulation and process modelling\simulation of adsorption-based post-combustion carbon capture for power plants: A state-of-the-art review," Applied Energy, Elsevier, vol. 317(C).
    21. Vo Thanh, Hung & Yasin, Qamar & Al-Mudhafar, Watheq J. & Lee, Kang-Kun, 2022. "Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers," Applied Energy, Elsevier, vol. 314(C).
    22. Khalilpour, Rajab, 2014. "Multi-level investment planning and scheduling under electricity and carbon market dynamics: Retrofit of a power plant with PCC (post-combustion carbon capture) processes," Energy, Elsevier, vol. 64(C), pages 172-186.
    23. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
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