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Development of a mathematical methodology to investigate biohydrogen production from regional and national agricultural crop residues: A case study of Iran

Asadi, N ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1016/j.ijhydene.2016.10.021
  3. Publisher: Elsevier Ltd , 2017
  4. Abstract:
  5. This study aims to construct a quantitative framework to assess biological production of hydrogen from agricultural residues in a country or region. The presented model is able to determine proper crops for biohydrogen production, its possible applications and use as well as environmental aspects. A multiplicative decomposition method was designed to forecast future production and Monte Carlo simulation was employed in the model to evaluate the risk of estimations. From 2013 to 2050, the hydrogen production capacity could increase from 53.59 to 164.41 kilotonnes (kt) in Iran. The highest contribution to biohydrogen production (52.1% in 2013 and 73.3% in 2050) belongs to cereal crops including wheat, barley, rice and corn and the share of horticultural products including apples, grapes and dates is the lowest (2.7% in 2013 and 2.2% in 2050). For possible variations in the quantity of collectable residue and biohydrogen yield, the production may change in the range of 40.16% and 209.48% of the base value in 2013 and 41.64% and 233.18% of that in 2050. Ammonia production as nitrogen fertilizer and the area could be cultivated by that for each crop were calculated. The amount of natural gas saving and reduction in greenhouse gas (GHG) emissions using biohydrogen were discussed. Development of hydrogen fuel cell vehicles and their impacts on the environment and consequent social costs as well as the quantity of gasoline would be saved were estimated by 2050. © 2016 Hydrogen Energy Publications LLC
  6. Keywords:
  7. Agricultural residues ; Biohydrogen ; Greenhouse gas emissions ; Monte Carlo simulation ; Time series ; Agricultural wastes ; Agriculture ; Carbon ; Cereal products ; Crashworthiness ; Crops ; Estimation ; Fuel cells ; Gas emissions ; Greenhouse gases ; Intelligent systems ; Monte Carlo methods ; Nitrogen fertilizers ; Photobiological hydrogen production ; Risk perception ; Agricultural crop residues ; Bio-hydrogen ; Bio-hydrogen production ; Horticultural products ; Hydrogen fuel cell vehicles ; Multiplicative decomposition ; Quantitative frameworks ; Social cost of carbon ; Hydrogen production
  8. Source: International Journal of Hydrogen Energy ; Volume 42, Issue 4 , 2017 , Pages 1989-2007 ; 03603199 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0360319916330178