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Exploring multi-objective trade-offs in the design space of a waste heat recovery system

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  • Mokhtar, Maizura
  • Burns, Stephen
  • Ross, Dave
  • Hunt, Ian

Abstract

A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy. In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large. To address this problem, a multiobjective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clustering algorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal designs. As a case study, a simulation of a WHRS commonly found in the food and drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system’s parameters and their trade-offs, providing another source of information for prospective installations.

Suggested Citation

  • Mokhtar, Maizura & Burns, Stephen & Ross, Dave & Hunt, Ian, 2017. "Exploring multi-objective trade-offs in the design space of a waste heat recovery system," Applied Energy, Elsevier, vol. 195(C), pages 114-124.
    • Handle: RePEc:eee:appene:v:195:y:2017:i:c:p:114-124
    DOI: 10.1016/j.apenergy.2017.03.030
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    1. Fazlollahi, Samira & Becker, Gwenaelle & Ashouri, Araz & Maréchal, François, 2015. "Multi-objective, multi-period optimization of district energy systems: IV – A case study," Energy, Elsevier, vol. 84(C), pages 365-381.
    2. Walmsley, Timothy G. & Walmsley, Michael R.W. & Atkins, Martin J. & Neale, James R., 2014. "Integration of industrial solar and gaseous waste heat into heat recovery loops using constant and variable temperature storage," Energy, Elsevier, vol. 75(C), pages 53-67.
    3. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    4. Muhsen, Dhiaa Halboot & Ghazali, Abu Bakar & Khatib, Tamer & Abed, Issa Ahmed & Natsheh, Emad M., 2016. "Sizing of a standalone photovoltaic water pumping system using a multi-objective evolutionary algorithm," Energy, Elsevier, vol. 109(C), pages 961-973.
    5. Falke, Tobias & Krengel, Stefan & Meinerzhagen, Ann-Kathrin & Schnettler, Armin, 2016. "Multi-objective optimization and simulation model for the design of distributed energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1508-1516.
    6. Wang, Jiangfeng & Yan, Zhequan & Wang, Man & Ma, Shaolin & Dai, Yiping, 2013. "Thermodynamic analysis and optimization of an (organic Rankine cycle) ORC using low grade heat source," Energy, Elsevier, vol. 49(C), pages 356-365.
    7. Zhang, Chuan & Zhou, Li & Chhabra, Pulkit & Garud, Sushant S. & Aditya, Kevin & Romagnoli, Alessandro & Comodi, Gabriele & Dal Magro, Fabio & Meneghetti, Antonella & Kraft, Markus, 2016. "A novel methodology for the design of waste heat recovery network in eco-industrial park using techno-economic analysis and multi-objective optimization," Applied Energy, Elsevier, vol. 184(C), pages 88-102.
    8. Mahbub, Md Shahriar & Cozzini, Marco & Østergaard, Poul Alberg & Alberti, Fabrizio, 2016. "Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design," Applied Energy, Elsevier, vol. 164(C), pages 140-151.
    9. Alarcon-Rodriguez, Arturo & Ault, Graham & Galloway, Stuart, 2010. "Multi-objective planning of distributed energy resources: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1353-1366, June.
    10. Tulus, Victor & Boer, Dieter & Cabeza, Luisa F. & Jiménez, Laureano & Guillén-Gosálbez, Gonzalo, 2016. "Enhanced thermal energy supply via central solar heating plants with seasonal storage: A multi-objective optimization approach," Applied Energy, Elsevier, vol. 181(C), pages 549-561.
    11. Evins, Ralph, 2013. "A review of computational optimisation methods applied to sustainable building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 230-245.
    12. Khoroshiltseva, Marina & Slanzi, Debora & Poli, Irene, 2016. "A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices," Applied Energy, Elsevier, vol. 184(C), pages 1400-1410.
    13. Stamp, Jane & Majozi, Thokozani, 2011. "Optimum heat storage design for heat integrated multipurpose batch plants," Energy, Elsevier, vol. 36(8), pages 5119-5131.
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    Cited by:

    1. Pashchenko, Dmitry, 2018. "First law energy analysis of thermochemical waste-heat recuperation by steam methane reforming," Energy, Elsevier, vol. 143(C), pages 478-487.

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