IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v239y2022ipcs0360544221022714.html
   My bibliography  Save this article

Systematic energy and exergy assessment of a hydropurification process: Theoretical and practical insights

Author

Listed:
  • Azarpour, Abbas
  • Mohamadi-Baghmolaei, Mohamad
  • Hajizadeh, Abdollah
  • Zendehboudi, Sohrab

Abstract

Improvement of process efficiency and reduction of operation costs are of significant importance in industrial chemical plants. In this study, an effective approach is proposed to analyze a hydropurification process, focusing on energy utilization, operating costs, environmental impact (e.g., CO2 emissions), and exergy performance. The hydropurification process includes heat exchangers, pumps, crystallizers, reactor, and furnace. This process is operated under high pressures and temperatures; it is prone to high consumption of energy and considerable loss of work quality. The vital operating parameters affecting the system performance are chosen by employing Taguchi method. The process simulation and/or thermodynamic modeling are carried out using Aspen Plus® software, and by writing codes for the exergy calculation in MATLAB software environment. The results are in acceptable agreement with the industrial data. The optimal conditions lead to 15% reduction in the process exergy destruction. The optimal operation can be established by implementing lower minimum approach temperature (ΔTmin) of the reaction system feed preheaters (e.g., 5 °C), setting a higher operating pressure in the heat exchangers of the feed preparation section (e.g., 96 bar), and enhancing the performance of heat exchangers using oil as heat transfer medium (optimizing furnace operating conditions). Furthermore, the performance of the fourth and sixth heat exchangers can be enhanced by 33.3%; the performance of the fourth crystallizer can be improved up to 18.7%. The furnace, which utilizes the expensive Therminol® 66 (as the heat transfer fluid), experiences a significant exergy destruction due to significant fluctuations in temperature and thermodynamic irreversibility in the combustion process. Implementation of the proposed optimal conditions can lower the operation costs and carbon tax by 9.96% ($20.5/h) and 14.75% ($14.54/h), respectively; and the CO2 emission rate can be decreased by 0.582 t/h. A decrease in ΔTmin of the heat exchangers improves the process specific energy consumption and exergy destruction. Moreover, effective control of the plant and avoidance of high fluctuations in the process parameters can lead to exergy destruction reduction.

Suggested Citation

  • Azarpour, Abbas & Mohamadi-Baghmolaei, Mohamad & Hajizadeh, Abdollah & Zendehboudi, Sohrab, 2022. "Systematic energy and exergy assessment of a hydropurification process: Theoretical and practical insights," Energy, Elsevier, vol. 239(PC).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221022714
    DOI: 10.1016/j.energy.2021.122023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221022714
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122023?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. Geng, Zhiqiang & Li, Hongda & Zhu, Qunxiong & Han, Yongming, 2018. "Production prediction and energy-saving model based on Extreme Learning Machine integrated ISM-AHP: Application in complex chemical processes," Energy, Elsevier, vol. 160(C), pages 898-909.
    2. Bolt, Andre & Dincer, Ibrahim & Agelin-Chaab, Martin, 2020. "Energy and exergy analyses of hydrogen production process with aluminum and water chemical reaction," Energy, Elsevier, vol. 205(C).
    3. Sarker, Md. Sazzat Hossain & Ibrahim, Mohd Nordin & Abdul Aziz, Norashikin & Punan, Mohd Salleh, 2015. "Energy and exergy analysis of industrial fluidized bed drying of paddy," Energy, Elsevier, vol. 84(C), pages 131-138.
    4. Fan, Guangli & Ahmadi, A. & Ehyaei, M.A. & Das, Biplab, 2021. "Energy, exergy, economic and exergoenvironmental analyses of polygeneration system integrated gas cycle, absorption chiller, and Copper-Chlorine thermochemical cycle to produce power, cooling, and hyd," Energy, Elsevier, vol. 222(C).
    5. Song, Chenxi & Li, Mingjia & Wen, Zhexi & He, Ya-Ling & Tao, Wen-Quan & Li, Yangzhe & Wei, Xiangyang & Yin, Xiaolan & Huang, Xing, 2014. "Research on energy efficiency evaluation based on indicators for industry sectors in China," Applied Energy, Elsevier, vol. 134(C), pages 550-562.
    6. Razi, Faran & Dincer, Ibrahim & Gabriel, Kamiel, 2020. "Energy and exergy analyses of a new integrated thermochemical copper-chlorine cycle for hydrogen production," Energy, Elsevier, vol. 205(C).
    7. Gong, Hong-Fei & Chen, Zhong-Sheng & Zhu, Qun-Xiong & He, Yan-Lin, 2017. "A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries," Applied Energy, Elsevier, vol. 197(C), pages 405-415.
    8. Utlu, Zafer & Hepbasli, Arif, 2007. "A review and assessment of the energy utilization efficiency in the Turkish industrial sector using energy and exergy analysis method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(7), pages 1438-1459, September.
    9. Wu, Junnian & Wang, Ruiqi & Pu, Guangying & Qi, Hang, 2016. "Integrated assessment of exergy, energy and carbon dioxide emissions in an iron and steel industrial network," Applied Energy, Elsevier, vol. 183(C), pages 430-444.
    10. Geng, ZhiQiang & Qin, Lin & Han, YongMing & Zhu, QunXiong, 2017. "Energy saving and prediction modeling of petrochemical industries: A novel ELM based on FAHP," Energy, Elsevier, vol. 122(C), pages 350-362.
    11. Akvile Lawrence & Patrik Thollander & Mariana Andrei & Magnus Karlsson, 2019. "Specific Energy Consumption/Use (SEC) in Energy Management for Improving Energy Efficiency in Industry: Meaning, Usage and Differences," Energies, MDPI, vol. 12(2), pages 1-22, January.
    12. Yuan, Benfeng & Zhang, Yu & Du, Wenli & Wang, Meihong & Qian, Feng, 2019. "Assessment of energy saving potential of an industrial ethylene cracking furnace using advanced exergy analysis," Applied Energy, Elsevier, vol. 254(C).
    13. Braimakis, Konstantinos & Magiri-Skouloudi, Despina & Grimekis, Dimitrios & Karellas, Sotirios, 2020. "Εnergy-exergy analysis of ultra-supercritical biomass-fuelled steam power plants for industrial CHP, district heating and cooling," Renewable Energy, Elsevier, vol. 154(C), pages 252-269.
    14. Bai, Wengang & Li, Hongzhi & Zhang, Lei & Zhang, Yifan & Yang, Yu & Zhang, Chun & Yao, Mingyu, 2021. "Energy and exergy analyses of an improved recompression supercritical CO2 cycle for coal-fired power plant," Energy, Elsevier, vol. 222(C).
    15. Xu, Yuan & Zhang, Mingqing & Ye, Liangliang & Zhu, Qunxiong & Geng, Zhiqiang & He, Yan-Lin & Han, Yongming, 2018. "A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction," Energy, Elsevier, vol. 164(C), pages 137-146.
    16. Abdelaziz, E.A. & Saidur, R. & Mekhilef, S., 2011. "A review on energy saving strategies in industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 150-168, January.
    17. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling using an effective latent variable based functional link learning machine," Energy, Elsevier, vol. 162(C), pages 883-891.
    18. Mohamadi-Baghmolaei, Mohamad & Hajizadeh, Abdollah & Zahedizadeh, Parviz & Azin, Reza & Zendehboudi, Sohrab, 2021. "Evaluation of hybridized performance of amine scrubbing plant based on exergy, energy, environmental, and economic prospects: A gas sweetening plant case study," Energy, Elsevier, vol. 214(C).
    19. Obara, Shin'ya, 2019. "Energy and exergy flows of a hydrogen supply chain with truck transportation of ammonia or methyl cyclohexane," Energy, Elsevier, vol. 174(C), pages 848-860.
    20. Saidur, R. & BoroumandJazi, G. & Mekhilef, S. & Mohammed, H.A., 2012. "A review on exergy analysis of biomass based fuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1217-1222.
    21. Sevinchan, Eren & Dincer, Ibrahim & Lang, Haoxiang, 2019. "Energy and exergy analyses of a biogas driven multigenerational system," Energy, Elsevier, vol. 166(C), pages 715-723.
    22. BoroumandJazi, G. & Rismanchi, B. & Saidur, R., 2013. "A review on exergy analysis of industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 198-203.
    23. Geng, Zhiqiang & Yang, Xiao & Han, Yongming & Zhu, Qunxiong, 2017. "Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: Application to complex chemical processes," Energy, Elsevier, vol. 120(C), pages 67-78.
    24. Qureshy, Ali M.M.I. & Dincer, Ibrahim, 2020. "Energy and exergy analyses of an integrated renewable energy system for hydrogen production," Energy, Elsevier, vol. 204(C).
    Full references (including those not matched with items on IDEAS)

    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. AlZahrani, Abdullah A. & Dincer, Ibrahim, 2022. "Assessment of a thin-electrolyte solid oxide cell for hydrogen production," Energy, Elsevier, vol. 243(C).
    2. Han, Yongming & Wu, Hao & Geng, Zhiqiang & Zhu, Qunxiong & Gu, Xiangbai & Yu, Bin, 2020. "Review: Energy efficiency evaluation of complex petrochemical industries," Energy, Elsevier, vol. 203(C).
    3. Raúl Arango-Miranda & Robert Hausler & Rabindranarth Romero-López & Mathias Glaus & Sara Patricia Ibarra-Zavaleta, 2018. "An Overview of Energy and Exergy Analysis to the Industrial Sector, a Contribution to Sustainability," Sustainability, MDPI, vol. 10(1), pages 1-19, January.
    4. Alexander Kramer & Fernando Morgado‐Dias, 2020. "Artificial intelligence in process control applications and energy saving: a review and outlook," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(6), pages 1133-1150, December.
    5. Li, Feng & Chu, Mansheng & Tang, Jue & Liu, Zhenggen & Guo, Jun & Yan, Ruijun & Liu, Peijun, 2022. "Thermodynamic performance analysis and environmental impact assessment of an integrated system for hydrogen generation and steelmaking," Energy, Elsevier, vol. 241(C).
    6. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).
    7. Charalampos Michalakakis & Jeremy Fouillou & Richard C. Lupton & Ana Gonzalez Hernandez & Jonathan M. Cullen, 2021. "Calculating the chemical exergy of materials," Journal of Industrial Ecology, Yale University, vol. 25(2), pages 274-287, April.
    8. BoroumandJazi, G. & Rismanchi, B. & Saidur, R., 2013. "A review on exergy analysis of industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 198-203.
    9. Singh, Neeraj Kumar & Kumari, Priyanka & Singh, Rajesh, 2021. "Intensified hydrogen yield using hydrogenase rich sulfate-reducing bacteria in bio-electrochemical system," Energy, Elsevier, vol. 219(C).
    10. Gollangi, Raju & K, NagamalleswaraRao, 2022. "Energy, exergy analysis of conceptually designed monochloromethane production process from hydrochlorination of methanol," Energy, Elsevier, vol. 239(PA).
    11. Zhu, Qun-Xiong & Zhang, Chen & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry," Applied Energy, Elsevier, vol. 213(C), pages 322-333.
    12. Mafakheri, Aso & Sulaimany, Sadegh & Mohammadi, Sara, 2023. "Predicting the establishment and removal of global trade relations for import and export of petrochemical products," Energy, Elsevier, vol. 269(C).
    13. Jahromi, Farid Sadeghian & Beheshti, Masoud & Rajabi, Razieh Fereydon, 2018. "Comparison between differential evolution algorithms and response surface methodology in ethylene plant optimization based on an extended combined energy - exergy analysis," Energy, Elsevier, vol. 164(C), pages 1114-1134.
    14. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
    15. Na, Hongming & Sun, Jingchao & Qiu, Ziyang & He, Jianfei & Yuan, Yuxing & Yan, Tianyi & Du, Tao, 2021. "A novel evaluation method for energy efficiency of process industry — A case study of typical iron and steel manufacturing process," Energy, Elsevier, vol. 233(C).
    16. Beisheim, Benedikt & Krämer, Stefan & Engell, Sebastian, 2020. "Hierarchical aggregation of energy performance indicators in continuous production processes," Applied Energy, Elsevier, vol. 264(C).
    17. Raúl Arango-Miranda & Robert Hausler & Rabindranarth Romero-Lopez & Mathias Glaus & Sara P. Ibarra-Zavaleta, 2018. "Carbon Dioxide Emissions, Energy Consumption and Economic Growth: A Comparative Empirical Study of Selected Developed and Developing Countries. “The Role of Exergy”," Energies, MDPI, vol. 11(10), pages 1-16, October.
    18. Wang, Yihan & Chen, Chen & Tao, Yuan & Wen, Zongguo & Chen, Bin & Zhang, Hong, 2019. "A many-objective optimization of industrial environmental management using NSGA-III: A case of China’s iron and steel industry," Applied Energy, Elsevier, vol. 242(C), pages 46-56.
    19. Santhanam, S. & Heddrich, M.P. & Riedel, M. & Friedrich, K.A., 2017. "Theoretical and experimental study of Reversible Solid Oxide Cell (r-SOC) systems for energy storage," Energy, Elsevier, vol. 141(C), pages 202-214.
    20. Utlu, Zafer, 2015. "Investigation of the potential for heat recovery at low, medium, and high stages in the Turkish industrial sector (TIS): An application," Energy, Elsevier, vol. 81(C), pages 394-405.

    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:239:y:2022:i:pc:s0360544221022714. 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.