JOURNAL ARTICLE

Performance Analysis of a Novel Water/Electricity Cogeneration System Based on Plasma Waste Gasification and a Small Modular Reactor.

  • Published In: Energy Technology, 2023, v. 11, n. 1. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Wu, Haoran; Chen, Heng; Yuan, Xin; Li, Tongyu; Pan, Peiyuan; Xu, Gang; Zhao, Qinxin; Wang, Xiuyan 3 of 3

Abstract

A hybrid system consisting of plasma gasification, multistage flash (MSF) desalination, and a small modular reactor (SMR) power plant is proposed for power and water cogeneration. In the novel configuration, the syngas from the plasma gasifier is burned to drive the gas turbine to generate electricity. The exhaust from the gas turbine is used to heat the steam of the SMR power plant and the seawater. The new design is thoroughly evaluated from a thermodynamic and economic point of view. In the hybrid system, 36.12 MW of electricity is generated from medical waste, and 23.75 MW of heat that can be utilized by MSF desalination is also generated from medical waste, with waste‐to‐electricity efficiency and energy efficiency of medical waste being 40.39% and 52.88%, respectively. The exergy efficiency of medical waste is 37.99%. In addition, the new design has a short dynamic payback period of 3.7 years, and the project's net present value can reach 430036.07 k$ over a 20 year lifetime; internal rate of return can reach 54%. Meanwhile, the effects of equivalence ratio and plasma temperature on the performance of the hybrid system are discussed. These results confirm the feasibility of the new design. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Energy Technology. 2023/01, Vol. 11, Issue 1, p1
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2023
  • ISSN:21944288
  • DOI:10.1002/ente.202200838
  • Accession Number:161181101
  • Copyright Statement:Copyright of Energy Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.