Power prediction and packed bed heat storage control for marine diesel engine waste heat recovery

Tiancheng Ouyang, Mingming Pan, Xianlin Tan, Lulu Li, Youbin Huang and Chunlan Mo

Applied Energy, 2024, vol. 357, issue C, No S0306261923018846

Abstract: The generation capability of the waste heat recovery system cannot be fully utilized by relying solely on the exhaust gas, as the mass flow rate and temperature of exhaust fluctuate in real time. The volatility of ship engine conditions and the instability of power demand can pose a challenge to real-time power allocation. Developing integrated prediction and control methods for power allocation becomes necessary. An integrated system is devised to fully utilize waste energy from marine diesel engines by combining a regenerative organic Rankine cycle with a controllable packed bed. The trends of thermodynamic properties and dynamic charging and discharging processes of the system are analyzed. To provide a plausible management strategy for allocating power in real time, a bidirectional long-short-term memory network power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise and improved marine predator algorithm is proposed. It is found that 315.6 kW of power can be obtained from the system, and its payback period is 6.84 years. In addition, by using the proposed model, the system can reduce diesel engine power generation. The findings suggest the proposed solution is a viable and meaningful strategy.

Keywords: Regenerative ORC; Packed bed control; ICEEMDAN-iMPA-BiLSTM; Marine diesel engine (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261923018846
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018846

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2023.122520

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().