IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v196y2020ics0360544220301778.html
   My bibliography  Save this article

Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization

Author

Listed:
  • Wu, Xiao
  • Shen, Jiong
  • Wang, Meihong
  • Lee, Kwang Y.

Abstract

This paper develops an intelligent predictive controller (IPC) for a large-scale solvent-based post-combustion CO2 capture (PCC) process. An artificial neural network (NN) model is trained to represent the dynamics of the PCC process based on an in-depth behavior investigation of the process under different operating conditions. The resulting NN model can portray the PCC characteristics very well in terms of dynamic trend, response time and steady-state gain. An intelligent predictive controller is thus developed based on the NN model to track the desired CO2 capture level and maintain the given re-boiler temperature, in which the particle swarm optimization (PSO) algorithm is applied to find the best future control sequence for the PCC process. A warm start scheme is proposed in the IPC to improve the quality of initial swarm in the PSO. Dynamic simulations to change CO2 capture level set-point and flue gas flow rate are carried out on the PCC process. The results show that the IPC can adjust CO2 capture level fast and significantly reduce the fluctuations in re-boiler temperature. It is concluded that the proposed IPC is helpful for flexible operation of the solvent-based PCC process.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220301778
    DOI: 10.1016/j.energy.2020.117070
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220301778
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.117070?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hou, Guolian & Xiong, Jian & Zhou, Guiping & Gong, Linjuan & Huang, Congzhi & Wang, Shunjiang, 2021. "Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network," Energy, Elsevier, vol. 234(C).
    2. Cui, Chengcheng & Zhang, Junli & Shen, Jiong, 2023. "System-level modeling, analysis and coordinated control design for the pressurized water reactor nuclear power system," Energy, Elsevier, vol. 283(C).
    3. Wu, Xiao & Wang, Meihong & Lee, Kwang Y., 2020. "Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control," Energy, Elsevier, vol. 206(C).
    4. Fu, Yue & Wang, Liyuan & Liu, Ming & Wang, Jinshi & Yan, Junjie, 2023. "Performance analysis of coal-fired power plants integrated with carbon capture system under load-cycling operation conditions," Energy, Elsevier, vol. 276(C).
    5. Akinola, Toluleke E. & Oko, Eni & Wu, Xiao & Ma, Keming & Wang, Meihong, 2020. "Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process," Energy, Elsevier, vol. 213(C).
    6. Milani, Dia & Luu, Minh Tri & Nelson, Scott & Abbas, Ali, 2022. "Process control strategies for solar-powered carbon capture under transient solar conditions," Energy, Elsevier, vol. 239(PE).
    7. Zhang, Yi & Liu, Jinfeng & Yang, Tingting & Liu, Jianbang & Shen, Jiong & Fang, Fang, 2021. "Dynamic modeling and control of direct air-cooling condenser pressure considering couplings with adjacent systems," Energy, Elsevier, vol. 236(C).
    8. Wang, Kangcheng & Zhang, Jie & Shang, Chao & Huang, Dexian, 2021. "Operation optimization of Shell coal gasification process based on convolutional neural network models," Applied Energy, Elsevier, vol. 292(C).
    9. Hosseini-Ardali, Seyed Mohsen & Hazrati-Kalbibaki, Majid & Fattahi, Moslem & Lezsovits, Ferenc, 2020. "Multi-objective optimization of post combustion CO2 capture using methyldiethanolamine (MDEA) and piperazine (PZ) bi-solvent," Energy, Elsevier, vol. 211(C).
    10. Basu, M., 2022. "Fuel constrained combined heat and power dynamic dispatch using horse herd optimization algorithm," Energy, Elsevier, vol. 246(C).
    11. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).
    12. Skjervold, Vidar T. & Mondino, Giorgia & Riboldi, Luca & Nord, Lars O., 2023. "Investigation of control strategies for adsorption-based CO2 capture from a thermal power plant under variable load operation," Energy, Elsevier, vol. 268(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Xiao & Wang, Meihong & Lee, Kwang Y., 2020. "Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control," Energy, Elsevier, vol. 206(C).
    2. 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).
    3. Wu, Xiao & Xi, Han & Ren, Yuning & Lee, Kwang Y., 2021. "Power-carbon coordinated control of BFG-fired CCGT power plant integrated with solvent-based post-combustion CO2 capture," Energy, Elsevier, vol. 226(C).
    4. Akinola, Toluleke E. & Oko, Eni & Wu, Xiao & Ma, Keming & Wang, Meihong, 2020. "Nonlinear model predictive control (NMPC) of the solvent-based post-combustion CO2 capture process," Energy, Elsevier, vol. 213(C).
    5. Tang, Zihan & Wu, Xiao, 2023. "Distributed predictive control guided by intelligent reboiler steam feedforward for the coordinated operation of power plant-carbon capture system," Energy, Elsevier, vol. 267(C).
    6. Otitoju, Olajide & Oko, Eni & Wang, Meihong, 2021. "Technical and economic performance assessment of post-combustion carbon capture using piperazine for large scale natural gas combined cycle power plants through process simulation," Applied Energy, Elsevier, vol. 292(C).
    7. Zhang, Yi & Liu, Jinfeng & Yang, Tingting & Liu, Jianbang & Shen, Jiong & Fang, Fang, 2021. "Dynamic modeling and control of direct air-cooling condenser pressure considering couplings with adjacent systems," Energy, Elsevier, vol. 236(C).
    8. Ren, Siyue & Feng, Xiao & Wang, Yufei, 2021. "Emergy evaluation of the integrated gasification combined cycle power generation systems with a carbon capture system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    9. Julio, Alisson Aparecido Vitoriano & Castro-Amoedo, Rafael & Maréchal, François & González, Aldemar Martínez & Escobar Palacio, José Carlos, 2023. "Exergy and economic analysis of the trade-off for design of post-combustion CO2 capture plant by chemical absorption with MEA," Energy, Elsevier, vol. 280(C).
    10. Ilea, Flavia-Maria & Cormos, Ana-Maria & Cristea, Vasile-Mircea & Cormos, Calin-Cristian, 2023. "Enhancing the post-combustion carbon dioxide carbon capture plant performance by setpoints optimization of the decentralized multi-loop and cascade control system," Energy, Elsevier, vol. 275(C).
    11. Zhu, Hengyi & Tan, Peng & He, Ziqian & Ma, Lun & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2023. "Revealing steam temperature characteristics for a double-reheat unit under coal calorific value variation," Energy, Elsevier, vol. 283(C).
    12. 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.
    13. 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.
    14. 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.
    15. Vo, Nguyen Dat & Oh, Dong Hoon & Kang, Jun-Ho & Oh, Min & Lee, Chang-Ha, 2020. "Dynamic-model-based artificial neural network for H2 recovery and CO2 capture from hydrogen tail gas," Applied Energy, Elsevier, vol. 273(C).
    16. Andrés Meana-Fernández & Juan M. González-Caballín & Roberto Martínez-Pérez & Francisco J. Rubio-Serrano & Antonio J. Gutiérrez-Trashorras, 2022. "Power Plant Cycles: Evolution towards More Sustainable and Environmentally Friendly Technologies," Energies, MDPI, vol. 15(23), pages 1-27, November.
    17. Liu, W. & Ji, Y. & Wang, R.Q. & Zhang, X.J. & Jiang, L., 2023. "Analysis on temperature vacuum swing adsorption integrated with heat pump for efficient carbon capture," Applied Energy, Elsevier, vol. 335(C).
    18. Song He & Yawen Zheng, 2024. "CO 2 Capture Cost Reduction Potential of the Coal-Fired Power Plants under High Penetration of Renewable Power in China," Energies, MDPI, vol. 17(9), pages 1-16, April.
    19. 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).
    20. Wilkes, Mathew Dennis & Brown, Solomon, 2022. "Flexible CO2 capture for open-cycle gas turbines via vacuum-pressure swing adsorption: A model-based assessment," Energy, Elsevier, vol. 250(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220301778. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.