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

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  • 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
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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).
    20. 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.
    21. 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.
    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).
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