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Sustainability Assessment of Biodiesel Supply Chain from Various Biomasses and Conversion Technologies

Author

Listed:
  • Sahar Safarian

    (Scientific and Technological Department of Presidential Office)

  • Sorena Sattari

    (Sharif University of Technology)

  • Zeinab Hamidzadeh

    (Scientific and Technological Department of Presidential Office)

Abstract

This paper develops a comprehensive sustainability assessment of producing biodiesel from five different types of feedstocks and totally 15 biomasses in Iran: (i) edible oil seeds (canola, soybean, olive, walnut, hazelnut, almond, sunflower, corn, and peanut), (ii) nonedible oil seeds (cotton and jatropha), (iii) waste cooking oil (WCO), (iv) animal fat (tallow and poultry), and (v) microalgae (Chlorella vulgaris). This assessment integrates the most significant economic, energy, environmental, and social aspects to compare and rank 15 biodiesel systems. The results show that WCO with production cost of 600$/tonbd, EUE of 2.8 and GHG emission of 0.35 ton CO2eq/tonbd is the top rank system from economic, energy and environmental perspectives, followed by tallow by having 876$/tonbd, 1.76 and 0.45 ton CO2eq/tonbd for production cost, EUE and GHG emission, respectively. Jatropha has also dominant statues socially and economic by having 7910 h/tonbd and 755$/tonbd for labor required and production cost, respectively but not suggested for Iran due to high water consumption (1053 m3/tonbd) and land use (10.9 ha/tonbd). Although algae takes up the top rank environmentally, it is not proposed because it has the highest production cost (5800$/tonbd) and very low job opportunity (65 h/tonbd). Finally, under different assessments, walnut and hazelnut have the higher ranks over the first-generation systems; however, in social aspect, cotton is in the high position.

Suggested Citation

  • Sahar Safarian & Sorena Sattari & Zeinab Hamidzadeh, 2018. "Sustainability Assessment of Biodiesel Supply Chain from Various Biomasses and Conversion Technologies," Biophysical Economics and Resource Quality, Springer, vol. 3(2), pages 1-15, June.
    • Handle: RePEc:spr:bioerq:v:3:y:2018:i:2:d:10.1007_s41247-018-0039-2
    DOI: 10.1007/s41247-018-0039-2
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    References listed on IDEAS

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    Cited by:

    1. Safarian, Sahar & Ebrahimi Saryazdi, Seyed Mohammad & Unnthorsson, Runar & Richter, Christiaan, 2020. "Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant," Energy, Elsevier, vol. 213(C).
    2. Safarian, Sahar & Unnthorsson, Runar & Richter, Christiaan, 2020. "Performance analysis and environmental assessment of small-scale waste biomass gasification integrated CHP in Iceland," Energy, Elsevier, vol. 197(C).
    3. Sahar Safarian & Sorena Sattari & Runar Unnthorsson & Zeinab Hamidzadeh, 2019. "Prioritization of Bioethanol Production Systems from Agricultural and Waste Agricultural Biomass Using Multi-criteria Decision Making," Biophysical Economics and Resource Quality, Springer, vol. 4(1), pages 1-16, March.
    4. Safarian, Sahar & Unnþórsson, Rúnar & Richter, Christiaan, 2019. "A review of biomass gasification modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 378-391.
    5. Kartal, Furkan & Özveren, Uğur, 2020. "A deep learning approach for prediction of syngas lower heating value from CFB gasifier in Aspen plus®," Energy, Elsevier, vol. 209(C).
    6. Sahar Safarian & Runar Unnthorsson & Christiaan Richter, 2020. "Techno-Economic and Environmental Assessment of Power Supply Chain by Using Waste Biomass Gasification in Iceland," Biophysical Economics and Resource Quality, Springer, vol. 5(2), pages 1-13, June.
    7. Sahar Safarian & Runar Unnthorsson, 2018. "An Assessment of the Sustainability of Lignocellulosic Bioethanol Production from Wastes in Iceland," Energies, MDPI, vol. 11(6), pages 1-16, June.
    8. Sahar Safarian & Seyed Mohammad Ebrahimi Saryazdi & Runar Unnthorsson & Christiaan Richter, 2021. "Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation," Biophysical Economics and Resource Quality, Springer, vol. 6(1), pages 1-13, March.

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