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Hydrogen management in refineries: Retrofitting of hydrogen networks, electricity and ammonia production

Rezaie, F ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1016/j.cep.2020.108118
  3. Publisher: Elsevier B.V , 2020
  4. Abstract:
  5. In this paper, hydrogen management in refineries is examined considering a new perspective to optimize refinery costs. In order to minimize the total annual cost in refineries a mixed-integer non-linear programming (MINLP) model has been developed. Retrofitting the hydrogen network and determining the hydrogen surplus capacity of refineries are the main outputs of the proposed model. Then, to utilize this surplus capacity and increase the profitability of refineries, surplus hydrogen is used to produce electricity and ammonia. The results of a case study showed that retrofitting of the hydrogen network can save 12% in the consumption of fresh hydrogen from hydrogen plants. After retrofitting of the existing hydrogen network, three scenarios for efficient use of hydrogen surplus are defined as electricity production by SOFC fuel cells, electricity production by hydrogen turbines, and ammonia production. The comparison of proposed scenarios showed that the ammonia production scenario has better economic results with a payback period of 3.52 years and 16.81 MUSD/yr. total saving. Furthermore, the sensitivity analysis of electricity prices in two electricity production scenarios was performed, and the results showed that by considering the exporting electricity price, electricity production scenarios are profitable due to having payback periods of about five years. © 2020
  6. Keywords:
  7. Hydrogen network ; Optimization ; Ammonia ; Integer programming ; Investments ; Nonlinear programming ; Profitability ; Retrofitting ; Sensitivity analysis ; Solid oxide fuel cells (SOFC) ; Ammonia production ; Economic results ; Electricity prices ; Electricity production ; Hydrogen management ; Hydrogen networks ; Mixed-integer nonlinear programming ; Surplus capacity ; Electric power generation
  8. Source: Chemical Engineering and Processing - Process Intensification ; Volume 157 , 2020
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0255270120305808