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Design of Robust Total Site Heat Recovery Loops via Monte Carlo Simulation

Author

Listed:
  • Florian Schlosser

    (Dep. Umweltgerechte Produkte und Prozesse, Universität Kassel, Kurt-Wolters-Straße 3, 34125 Kassel, Germany)

  • Ron-Hendrik Peesel

    (Dep. Umweltgerechte Produkte und Prozesse, Universität Kassel, Kurt-Wolters-Straße 3, 34125 Kassel, Germany)

  • Henning Meschede

    (Dep. Umweltgerechte Produkte und Prozesse, Universität Kassel, Kurt-Wolters-Straße 3, 34125 Kassel, Germany)

  • Matthias Philipp

    (Bayernwerk Natur GmbH, Carl-von-Linde-Straße 38, 85716 Unterschleißheim, Germany)

  • Timothy G. Walmsley

    (Sustainable Process Integration Laboratory–SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology-VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic)

  • Michael R. W. Walmsley

    (Energy Research Centre, School of Engineering, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand)

  • Martin J. Atkins

    (Energy Research Centre, School of Engineering, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand)

Abstract

For increased total site heat integration, the optimal sizing and robust operation of a heat recovery loop (HRL) are prerequisites for economic efficiency. However, sizing based on one representative time series, not considering the variability of process streams due to their discontinuous operation, often leads to oversizing. The sensitive evaluation of the performance of an HRL by Monte Carlo (MC) simulation requires sufficient historical data and performance models. Stochastic time series are generated by distribution functions of measured data. With these inputs, one can then model and reliably assess the benefits of installing a new HRL. A key element of the HRL is a stratified heat storage tank. Validation tests of a stratified tank (ST) showed sufficient accuracy with acceptable simulation time for the variable layer height (VLH) multi-node (MN) modelling approach. The results of the MC simulation of the HRL system show only minor yield losses in terms of heat recovery rate (HRR) for smaller tanks. In this way, costs due to oversizing equipment can be reduced by better understanding the energy-capital trade-off.

Suggested Citation

  • Florian Schlosser & Ron-Hendrik Peesel & Henning Meschede & Matthias Philipp & Timothy G. Walmsley & Michael R. W. Walmsley & Martin J. Atkins, 2019. "Design of Robust Total Site Heat Recovery Loops via Monte Carlo Simulation," Energies, MDPI, vol. 12(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:930-:d:212615
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    References listed on IDEAS

    as
    1. 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.
    2. Philipp, Matthias & Schumm, Gregor & Peesel, Ron-Hendrik & Walmsley, Timothy G. & Atkins, Martin J. & Schlosser, Florian & Hesselbach, Jens, 2018. "Optimal energy supply structures for industrial food processing sites in different countries considering energy transitions," Energy, Elsevier, vol. 146(C), pages 112-123.
    3. Baeten, Brecht & Confrey, Thomas & Pecceu, Sébastien & Rogiers, Frederik & Helsen, Lieve, 2016. "A validated model for mixing and buoyancy in stratified hot water storage tanks for use in building energy simulations," Applied Energy, Elsevier, vol. 172(C), pages 217-229.
    4. Meschede, Henning & Dunkelberg, Heiko & Stöhr, Fabian & Peesel, Ron-Hendrik & Hesselbach, Jens, 2017. "Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods," Energy, Elsevier, vol. 128(C), pages 86-100.
    5. Walmsley, Timothy G. & Walmsley, Michael R.W. & Tarighaleslami, Amir H. & Atkins, Martin J. & Neale, James R., 2015. "Integration options for solar thermal with low temperature industrial heat recovery loops," Energy, Elsevier, vol. 90(P1), pages 113-121.
    6. Heiko Dunkelberg & Maximilian Sondermann & Henning Meschede & Jens Hesselbach, 2019. "Assessment of Flexibilisation Potential by Changing Energy Sources Using Monte Carlo Simulation," Energies, MDPI, vol. 12(4), pages 1-24, February.
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    Cited by:

    1. Jiří Jaromír Klemeš & Petar Sabev Varbanov & Paweł Ocłoń & Hon Huin Chin, 2019. "Towards Efficient and Clean Process Integration: Utilisation of Renewable Resources and Energy-Saving Technologies," Energies, MDPI, vol. 12(21), pages 1-32, October.
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    3. Khairulnadzmi Jamaluddin & Sharifah Rafidah Wan Alwi & Khaidzir Hamzah & Jiří Jaromír Klemeš, 2020. "A Numerical Pinch Analysis Methodology for Optimal Sizing of a Centralized Trigeneration System with Variable Energy Demands," Energies, MDPI, vol. 13(8), pages 1-35, April.

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