Waste heat recovery system for marine engines optimized through a preference learning rank function embedded into a Bayesian optimizer

  1. Díaz-Secades, Luis Alfonso 1
  2. González, R. 1
  3. Rivera, N. 1
  4. Montañés, Elena 1
  5. Quevedo, José Ramón 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Ocean Engineering

ISSN: 0029-8018,  1873-5258

Año de publicación: 2023

Volumen: 281

Páginas: 114747

Tipo: Artículo

DOI: 10.1016/J.OCEANENG.2023.114747 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Ocean Engineering

Objetivos de desarrollo sostenible

Resumen

Waste heat recovery is a proven process to improve efficiency on engines and meets current necessities of the maritime industry. Since January 1, 2023, already built vessels must meet the energy efficiency indicators known as EEXI and CII. Aiming to reduce fuel consumption and mitigate pollution emissions, a novel waste heat recovery system composed of steam Rankine cycle, organic Rankine cycle, thermoelectric harvesters and desalination is presented. High, medium and low-grade waste heat from exhaust gas, jacket water, lubricating oil and engine block radiation are targeted for recovery. Performance assessment of each subsystem when implemented on a real case study 6-cylinder medium speed marine engine is analyzed. The equivalent electricity production concept was used for the assessment of the desalination subsystem. The proposed system effectively recovers waste energy, offering economic benefits, reducing pollution and satisfying the daily demand of fresh water. Also, optimal states of the waste heat recovery are provided via Bayesian optimization, which requires an evaluation function for the system to be optimized. However, this function is not available and cannot be straightforwardly established, since the quality of waste heat recovery depends on some indicators with a trade-off among them. Hence, a preference learning procedure that exploits expert knowledge is proposed to induce a function of this kind from those indicators in order to be embedded into the Bayesian optimization procedure afterward. Applied to the case study engine, a fuel consumption reduction of 15.04% is achieved. Fuel savings lead to an improvement in energy efficiency indicators, achieving a reduction of 6.98% on the EEXI and a 13.85% on the CII.

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Información de financiación

This research has been partially supported by the Spanish Ministry of Science and Innovation through the grant PID2019-110742RB-I00.

Financiadores

Referencias bibliográficas

  • Alfa Laval Copenhagen A/S, 2006. Instruction Manual for Freshwater Generator Type D- PU-36-C100.
  • Bahamonde, A., Díez, J., Quevedo, J.R., Luaces, O., del Coz, J.J., 2007. How to learn consumer preferences from the analysis of sensory data by means of support vector machines (SVM). Trends Food Sci. Technol. 18, 20–28. https://doi.org/10.1016/j. tifs.2006.07.014.
  • Baldasso, E., Mondejar, M.E., Andreasen, J.G., Rønnenfelt, K.A.T., Nielsen, B.Ø., Haglind, F., 2020. Design of organic Rankine cycle power systems for maritime applications accounting for engine backpressure effects. Appl. Therm. Eng. 178, 115527 https://doi.org/10.1016/j.applthermaleng.2020.115527.
  • Baldi, F., Larsen, U., Gabrielii, C., 2015. Comparison of different procedures for the optimisation of a combined Diesel engine and organic Rankine cycle system based on ship operational profile. Ocean. Eng. 110, 85–93. https://doi.org/10.1016/j. oceaneng.2015.09.037.
  • Bell, I.H., Wronski, J., Quoilin, S., Lemort, V., 2014. Pure and pseudo-pure fluid thermophysical property evaluation and the open-source thermophysical property library CoolProp. Ind. Eng. Chem. Res. 53, 2498–2508. https://doi.org/10.1021/ ie4033999.
  • Bøckmann, E., Steen, S., 2016. Calculation of EEDIweather for a general cargo vessel. Ocean. Eng. 122, 68–73. https://doi.org/10.1016/j.oceaneng.2016.06.007.
  • Butrymowicz, D., Gagan, J., Łukaszuk, M., ´Smierciew, K., Pawluczuk, A., Zieli´nski, T., Kędzierski, M., 2021. Experimental validation of new approach for waste heat recovery from combustion engine for cooling and heating demands from combustion engine for maritime applications. J. Clean. Prod. 290, 125206 https://doi.org/ 10.1016/j.jclepro.2020.125206.
  • Champier, D., 2017. Thermoelectric generators: a review of applications. Energy Convers. Manag. 140, 167–181. https://doi.org/10.1016/j.enconman.2017.02.070.
  • Czerma´nski, E., Oniszczuk-Jastrząbek, A., Spangenberg, E.F., Kozłowski, Ł., Adamowicz, M., Jankiewicz, J., Cirella, G.T., 2022. Implementation of the energy efficiency existing ship index: an important but costly step towards ocean protection. Mar. Pol. 145, 105259 https://doi.org/10.1016/j.marpol.2022.105259.
  • Delannoy, L., Longaretti, P.-Y., Murphy, D.J., Prados, E., 2021. Peak oil and the low- carbon energy transition: a net-energy perspective. Appl. Energy 304, 117843. https://doi.org/10.1016/j.apenergy.2021.117843.
  • Díaz-Secades, L.A., Gonz´alez, R., Rivera, N., 2022. Waste heat recovery from marine main medium speed engine block. Energy, exergy, economic and environmental (4E) assessment – case study. Ocean. Eng. 264, 112493 https://doi.org/10.1016/j. oceaneng.2022.112493.
  • DNV, 2021. CII - Carbon Intensity Indicator 1–19.
  • Emadi, M.A., Chitgar, N., Oyewunmi, O.A., Markides, C.N., 2020. Working-fluid selection and thermoeconomic optimisation of a combined cycle cogeneration dual- loop organic Rankine cycle (ORC) system for solid oxide fuel cell (SOFC) waste-heat recovery. Appl. Energy 261, 114384. https://doi.org/10.1016/j. apenergy.2019.114384.
  • Giannoutsos, S.V., Manias, S.N., 2016. Improving engine room ventilation systems: a data-driven process controller for energy-efficient, variable-speed fan operation in marine vessels. IEEE Ind. Appl. Mag. 22, 66–81. https://doi.org/10.1109/ MIAS.2015.2459088.
  • Gude, V.G., 2019. Thermal desalination of ballast water using onboard waste heat in marine industry. Int. J. Energy Res. 43, 6026–6037. https://doi.org/10.1002/ er.4647.
  • Gude, V.G., Nirmalakhandan, N., 2009. Desalination at low temperatures and low pressures. Desalination 244, 239–247. https://doi.org/10.1016/j. desal.2008.06.005.
  • Hærvig, J., Sørensen, K., Condra, T.J., 2016. Guidelines for optimal selection of working fluid for an organic Rankine cycle in relation to waste heat recovery. Energy 96, 592–602. https://doi.org/10.1016/j.energy.2015.12.098.
  • Herbrich, R., Graepel, T., Obermayer, K., 2000. Large margin rank boundaries for ordinal regression. Adv. Large Margin Classif. 88.
  • Hou, S., Cao, S., Yu, L., Zhou, Y., Wu, Y., Zhang, F., 2018. Performance optimization of combined supercritical CO2 recompression cycle and regenerative organic Rankine cycle using zeotropic mixture fluid. Energy Convers. Manag. 166, 187–200. https:// doi.org/10.1016/j.enconman.2018.04.025.
  • Marlow Industries, 2015. Technical Data Sheet for TG12-8 Single-Stage Thermoelectric Generator 1–2.
  • International Maritime Organization - IMO, 2021. MEPC.328 (76) - Resolution - Amendments to the Annex of the Protocol of 1997 - 2021 Revised MARPOL Annex VI 148, pp. 148–162.
  • International Maritime Organization (IMO), 2021a. Fourth IMO greenhouse gas study. Int. Marit. Organ. 951–952.
  • International Maritime Organization (IMO), 2021b. MEPC.1-Circ, p. 896.
  • International Maritime Organization (IMO), 2021c. Further shipping GHG emission reduction measures adopted [WWW Document]. URL. https://www.imo.org/en/MediaCentre/PressBriefings/pages/MEPC76.aspx. (Accessed 11 January 2022). https://www.imo.org/en/MediaCentre/PressBriefings/pages/MEPC76.aspx.
  • International Maritime Organization (IMO), 2021d. Resolution MEPC 334 (76).
  • International Maritime Organization (IMO), 2021e. Resolution MEPC 333 (76).
  • International Maritime Organization (IMO), 2021f. Resolution MEPC 335 (76).
  • International Maritime Organization (IMO), 2021g. Resolution MEPC 338 (76).
  • International Maritime Organization (IMO), 2021h. Resolution MEPC 337 (76).
  • International Maritime Organization (IMO), 2021i. Resolution MEPC 339 (76).
  • International Maritime Organization (IMO), 2022a. Resolution 1.1149(32) - Revised Strategic Plan for the Organization for the Six-Year Period 2018 to 2023 9, pp. 1–29.
  • International Maritime Organization (IMO), 2022b. Resolution MEPC 352 (78), 1–6, 352.
  • IPCC, 2006. Chapter 2.3: mobile combustion. 2006 IPCC guidel. Natl. Greenh. Gas Invent. 1–78.
  • IPCC, 2021. Climate Change 2021: the Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/10.1017/9781009157896.
  • Koshy, A.P., 2015. Exhaust gas waste heat recovery. Int. J. Innov. Res. Sci. Technol. 1, 392–400.
  • Lampe, J., Rüde, E., Papadopoulos, Y., Kabir, S., 2018. Model-based assessment of energy-efficiency, dependability, and cost-effectiveness of waste heat recovery systems onboard ship. Ocean. Eng. 157, 234–250. https://doi.org/10.1016/j. oceaneng.2018.03.062.
  • Lecompte, S., Huisseune, H., van den Broek, M., Vanslambrouck, B., De Paepe, M., 2015. Review of organic Rankine cycle (ORC) architectures for waste heat recovery. Renew. Sustain. Energy Rev. 47, 448–461. https://doi.org/10.1016/j. rser.2015.03.089.
  • Liu, X., Nguyen, M.Q., Chu, J., Lan, T., He, M., 2020a. A novel waste heat recovery system combing steam Rankine cycle and organic Rankine cycle for marine engine. J. Clean. Prod. 265, 121502 https://doi.org/10.1016/j.jclepro.2020.121502.
  • Liu, X., Nguyen, M.Q., Chu, J., Lan, T., He, M., 2020b. A novel waste heat recovery system combing steam Rankine cycle and organic Rankine cycle for marine engine. J. Clean. Prod. 265, 121502 https://doi.org/10.1016/j.jclepro.2020.121502.
  • Mallouppas, G., Yfantis, E.A., 2021. Decarbonization in Shipping industry: a review of research, technology development, and innovation proposals. J. Mar. Sci. Eng. 9 https://doi.org/10.3390/jmse9040415.
  • Masson-Delmotte, et al., 2021. IPCC, 2021: contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Clim. Chang. 2021 Phys. Sci. Basis. 3949.
  • Montazerinejad, H., Ahmadi, P., Montazerinejad, Z., 2019. Advanced exergy, exergo- economic and exrgo-environmental analyses of a solar based trigeneration energy system. Appl. Therm. Eng. 152, 666–685. https://doi.org/10.1016/j. applthermaleng.2019.01.040.
  • Nazari, N., Porkhial, S., 2020. Multi-objective optimization and exergo-economic assessment of a solar-biomass multi-generation system based on externally-fired gas turbine, steam and organic Rankine cycle, absorption chiller and multi-effect desalination. Appl. Therm. Eng. 179, 115521 https://doi.org/10.1016/j. applthermaleng.2020.115521.
  • Ng, C., Tam, I.C.K., Wu, D., 2020. Thermo-economic performance of an organic rankine cycle system recovering waste heat onboard an offshore service vessel. J. Mar. Sci. Eng. 8, 351. https://doi.org/10.3390/jmse8050351.
  • Nour Eddine, A., Chalet, D., Faure, X., Aixala, L., Chess´e, P., 2018. Optimization and characterization of a thermoelectric generator prototype for marine engine application. Energy 143, 682–695. https://doi.org/10.1016/j.energy.2017.11.018.
  • Ouyang, T., Su, Z., Gao, B., Pan, M., Chen, N., Huang, H., 2019. Design and modeling of marine diesel engine multistage waste heat recovery system integrated with flue-gas desulfurization. Energy Convers. Manag. 196, 1353–1368. https://doi.org/10.1016/ j.enconman.2019.06.065.
  • Ouyang, T., Su, Z., Yang, R., Li, C., Huang, H., Wei, Q., 2020. A framework for evaluating and optimizing the cascade utilization of medium-low grade waste heat in marine dual-fuel engines. J. Clean. Prod. 276, 123289 https://doi.org/10.1016/j. jclepro.2020.123289.
  • Ouyang, T., Huang, G., Lu, Y., Liu, B., Hu, X., 2021a. Multi-criteria assessment and optimization of waste heat recovery for large marine diesel engines. J. Clean. Prod. 309, 127307 https://doi.org/10.1016/j.jclepro.2021.127307.
  • Ouyang, T., Wang, Z., Zhao, Z., Lu, J., Zhang, M., 2021b. An advanced marine engine waste heat utilization scheme: electricity-cooling cogeneration system integrated with heat storage device. Energy Convers. Manag. 235, 113955 https://doi.org/ 10.1016/j.enconman.2021.113955.
  • Perifanis, T., 2022. How US suppliers alter their extraction rates and what this means for peak oil theory. Energies 15, 821. https://doi.org/10.3390/en15030821.
  • Quevedo, J.R., Montanés, E., 2009. Obtaining rubric weights for assessments by more than one lecturer using a pairwise learning model. In: EDM’09 - Educational Data Mining 2009: 2nd International Conference on Educational Data Mining, pp. 289–298.
  • Rose, C.D., 1983. Current design and applications of marine evaporators. Mar. Technol. SNAME News 20, 348–355. https://doi.org/10.5957/mt1.1983.20.4.348.
  • Sasa, K., Terada, D., Shiotani, S., Wakabayashi, N., Ikebuchi, T., Chen, C., Takayama, A., Uchida, M., 2015. Evaluation of ship performance in international maritime transportation using an onboard measurement system - in case of a bulk carrier in international voyages. Ocean. Eng. 104, 294–309. https://doi.org/10.1016/j. oceaneng.2015.05.015.
  • Schölkopf, B., Smola, A.J., Bach, F., others, 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and beyond. MIT press, Cambridge, MA, US.
  • Sellers, C., 2017. Field operation of a 125kW ORC with ship engine jacket water. In: V, D., A, G., M, A. (Eds.), 4th International Seminar on Organic Rankine Cycle (ORC) Power Systems, ORC 2017. Elsevier Ltd, Calnetix Technologies, LLC, 16232
  • Shoemaker Ave., Cerritos, CA 90703, United States, pp. 495–502. https://doi.org/ 10.1016/j.egypro.2017.09.168.
  • Singh, D.V., Pedersen, E., 2016. A review of waste heat recovery technologies for maritime applications. Energy Convers. Manag. 111, 315–328. https://doi.org/ 10.1016/j.enconman.2015.12.073.
  • Snoek, J., Larochelle, H., Adams, R.P., 2012. Practical Bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (Eds.), Advances in Neural Information Processing Systems. Curran Associates, Inc., pp. 2951–2959
  • Steck, H., Krishnapuram, B., Dehing-oberije, C., Lambin, P., Raykar, V.C., 2007. On ranking in survival analysis: bounds on the concordance index. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (Eds.), Advances in Neural Information Processing Systems. Curran Associates, Inc.
  • Tchanche, B.F., Lambrinos, G., Frangoudakis, A., Papadakis, G., 2011. Low-grade heat conversion into power using organic Rankine cycles – a review of various applications. Renew. Sustain. Energy Rev. 15, 3963–3979. https://doi.org/10.1016/ j.rser.2011.07.024.
  • Tohidi, F., Ghazanfari Holagh, S., Chitsaz, A., 2022. Thermoelectric Generators: a comprehensive review of characteristics and applications. Appl. Therm. Eng. 201, 117793 https://doi.org/10.1016/j.applthermaleng.2021.117793.
  • Turton, R., 2018. Analysis, Synthesis, and Design of Chemical Processes. Vapnik, V., 1998. Statistical Learning Theory. John Wiley, New York.
  • Ye, Z., Yang, J., Shi, J., Chen, J., 2020. Thermo-economic and environmental analysis of various low-GWP refrigerants in Organic Rankine cycle system. Energy 199, 117344. https://doi.org/10.1016/j.energy.2020.117344.
  • Zhu, S., Zhang, K., Deng, K., 2020. A review of waste heat recovery from the marine engine with highly efficient bottoming power cycles. Renew. Sustain. Energy Rev. 120, 109611 https://doi.org/10.1016/j.rser.2019.109611.
  • Wärtsilä 32, 2021. Product Guide. https://brandhub.wartsila.com/m/54000f75ef455 f3d/original/Wartsila-32-Product-guide.pdf?utm_source=engines&utm_mediu m=dieselengines&utm_term=marine-power&utm_content=productguide&utm_ca mpaign=mp-engines-and-generating-sets-gated.