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  • ارایه مدل‌های مدیریت زنجیره تأمین به‌منظور توسعه تولید سوخت سبز از جلبک‌ها در کشور

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کد مقاله : 13961 بازدید : 183 صفحه: 189 - 210

10.22034/jest.2019.13961

نوع مقاله: پژوهشی

ارایه مدل‌های مدیریت زنجیره تأمین به‌منظور توسعه تولید سوخت سبز از جلبک‌ها در کشور

محورهای موضوعی : مدیریت محیط زیست

شایان محسنی 1 , میر سامان پیشوایی 2

1 - کارشناسی ارشد، دانشکده مهندسی صنایع ، دانشگاه علم و صنعت ایران، تهران، ایران.
2 - دانشیار، دانشکده مهندسی صنایع، دانش‌گاه علم و صنعت ایران، تهران، ایران. *(مسوول مکاتبات)

تاریخ دریافت : 1394/09/26 تاریخ پذیرش : 1395/02/01 تاریخ انتشار : 1398/02/01

کلید واژه: انرژی تجدید پذیر, سوخت سبز, جلبک‌ها, بهینه سازی استوار, زنجیره تأمین زیست توده,

چکیده مقاله :

زمینه و هدف: آلودگی‌های زیست محیطی و وابستگی شدید به سوخت‌های آلاینده فسیلی از مهم‌ترین انگیزه‌های توسعه سوخت‌های سبز در کشور محسوب می‌شود. جلبک‌ها به‌عنوان یکی از جدیدترین مواد اولیه برای تولید سوخت سبز در دنیا معرفی شده‌اند. به علاوه در فرآیند رشد جلبک‌ها، گازهای انتشار یافته از نیروگاه‌های تولید برق به کار گرفته می‌شود که باعث کاهش بخش اعظم گازهای گل‌خانه‌ای انتشار یافته به اتمسفر می‌شود. روش بررسی: این مطالعه برای بررسی توسعه چنین سوخت‌هایی در کشور به ارایه مدل‌‌های طراحی و مدیریت زنجیره تأمین سوخت سبز بر‌پایه جلبک‌ها می‌پردازد. بر این اسا‌س در ابتدا یک مدل قطعی برای مدل سازی تمام فعالیت‌های زنجیره تأمین تولید سوخت سبز که شامل تأمین مواد اولیه لازم برای رشد جلبک‌ها، کشت جلبک‌ها و تبدیل آن‌ها به سوخت و نهایتاً عرضه سوخت در کشور است، توسعه داده می‌شود. سپس این مدل قطعی به یک مدل طراحی شبکه استوار برای دست‌یابی به تصمیمات زنجیره تأمین ایمن و استوار در برابر عدم قطعیت بسط داده می‌شود. یافته‌ها: نتایج به‌کارگیری مدل پیشنهادی برای توسعه سوخت‌های جلبکی در کشور نشان می‌دهد که تولید هر لیتر سوخت سبز در حال حاضر 88.5 هزار ریال می‌باشد. بحث و نتیجه‌گیری: هزینه کنونی تولید سوخت از جلبک ها توانایی رقابت با سوخت های فسیلی را ندارد اما این هزینه با افزایش اندک میزان رشد جلبک‌ها و محتوای روغنی آنها در آینده می‌تواند به ‌شدت کاهش پیدا کند.

چکیده انگلیسی:

Background and Objective: Environmental pollution and dependency on fossil fuels are the most important incentives for the development of biofuels in Iran. Microalgae are introduced as one of the best raw materials for the production of biofuels in the world. In addition, for the production of microalgae, the emissions from power plants are used which leads to the reduction of greenhouse gas emissions to the atmosphere. Method: This paper proposes a microalgae-based biofuel supply chain network design model to study the development of such fuels. First, a deterministic model was developed to model the all activities of the supply chain including provision of raw materials for the production of microalgae, microalgae cultivation, turning them into biofuel and eventually biofuel distribution. Then, the deterministic model was extended to a robust network design model to achieve a safe and stable supply chain decisions in the face of uncertainty. Findings: Results of using the proposed model for the development of microalgal biofuel production show that the cost biofuel production from microalgae is 88.5 thousand Rials per liter. Discussion and Conclusion: Current production cost of microalgae-based biofuel cannot compete with that of fossil fuel, but the cost can be significantly decreased with a slight increase in algae productivity or oil content in future.

منابع و مأخذ:


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  1. Yue D, You F, Snyder SW. Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers & Chemical Engineering. 2014;66:36-56.
    2.
  2. Sims RE, Mabee W, Saddler JN, Taylor M. An overview of second generation biofuel technologies. Bioresource technology. 2010;101(6):1570-80.
    3.
  3. Mata TM, Martins AA, Caetano NS. Microalgae for biodiesel production and other applications: a review. Renewable and sustainable energy reviews. 2010;14(1):217-32.
    4.
  4. Davis R, Aden A, Pienkos PT. Techno-economic analysis of autotrophic microalgae for fuel production. Applied Energy. 2011;88(10):3524-31.
    5.
  5. Maity JP, Bundschuh J, Chen C-Y, Bhattacharya P. Microalgae for third generation biofuel production, mitigation of greenhouse gas emissions and wastewater treatment: Present and future perspectives–A mini review. Energy. 2014;78:104-13.
    6.
  6. Balaman ŞY, Selim H. A network design model for biomass to energy supply chains with anaerobic digestion systems. Applied Energy. 2014;130:289-304.
    7.
  7. Sokhansanj S, Kumar A, Turhollow AF. Development and implementation of integrated biomass supply analysis and logistics model (IBSAL). Biomass and Bioenergy. 2006;30(10):838-47.
    8.
  8. Awudu I, Zhang J. Stochastic production planning for a biofuel supply chain under demand and price uncertainties. Applied Energy. 2013;103:189-96.
    9.
  9. An H, Wilhelm WE, Searcy SW. A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas. Bioresource technology. 2011;102(17):7860-70.
    10.
  10. Shabani N, Sowlati T, Ouhimmou M, Rönnqvist M. Tactical supply chain planning for a forest biomass power plant under supply uncertainty. Energy. 2014;78:346-55.
    11.
  11. Azadeh A, Arani HV, Dashti H. A stochastic programming approach towards optimization of biofuel supply chain. Energy. 2014;76:513-25.
    12.
  12. Zhang J, Osmani A, Awudu I, Gonela V. An integrated optimization model for switchgrass-based bioethanol supply chain. Applied Energy. 2013;102:1205-17.
    13.
  13. Osmani A, Zhang J. Economic and environmental optimization of a large scale sustainable dual feedstock lignocellulosic-based bioethanol supply chain in a stochastic environment. Applied Energy. 2014;114:572-87.
    14.
  14. Sadjadi S, Omrani H. Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies. Energy Policy. 2008;36(11):4247-54.
    15.
  15. Yang J, Xu M, Zhang X, Hu Q, Sommerfeld M, Chen Y. Life-cycle analysis on biodiesel production from microalgae: water footprint and nutrients balance. Bioresource technology. 2011;102(1):159-65.
    16.
  16. Pate R, Klise G, Wu B. Resource demand implications for US algae biofuels production scale-up. Applied Energy. 2011;88(10):3377-88.
    17.
  17. Soyster AL. Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations research. 1973;21(5):1154-7.
    18.
  18. Ben-Tal A, Nemirovski A. Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming. 2000;88(3):411-24.
    19.
  19. Li Z, Ding R, Floudas CA. A comparative theoretical and computational study on robust counterpart optimization: I. Robust linear optimization and robust mixed integer linear optimization. Industrial & engineering chemistry research. 2011;50(18):10567-603.
    20.
  20. Bertsimas D, Sim M. The price of robustness. Operations research. 2004;52(1):35-53.
    21.
  21. Najafi G, Ghobadian B, Yusaf TF. Algae as a sustainable energy source for biofuel production in Iran: A case study. Renewable and Sustainable Energy Reviews. 2011;15(8):3870-6.
    22.
  22. Tabatabaei M, Tohidfar M, Jouzani GS, Safarnejad M, Pazouki M. Biodiesel production from genetically engineered microalgae: Future of bioenergy in Iran. Renewable and Sustainable Energy Reviews. 2011;15(4):1918-27.
    23.
  23. Lundquist TJ, Woertz IC, Quinn N, Benemann JR. A realistic technology and engineering assessment of algae biofuel production. Energy Biosciences Institute. 2010:1.
    24.
  24. Kally E, Fishelson G. Water and peace: water resources and the Arab-Israeli peace process: Praeger; 1993.
    25.
  25. Doctor R, Palmer A, Coleman D, Davison J, Hendriks C, Kaarstad O, et al. Chapter 4: Transport of CO2. IPCC Special Report on Carbon Dioxide Capture and Storage, R. Pichs-Madruga, S. Timashev, Eds., Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge; 2005.
    26.
  26. Wigmosta MS, Coleman AM, Skaggs RJ, Huesemann MH, Lane LJ. National microalgae biofuel production potential and resource demand. Water Resources Research. 2011;47(3).
    27.
  27. Iran  Statistical  Yearbook  of  oil  products  consumption.National  Iranian  Oil  Products  Distribution Company (NIOPDC) 2012.
    28.
  28. Pishvaee MS, Rabbani M, Torabi SA. A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling. 2011;35(2):637-49.
    29.

 




 

 

 

 

 

 

 

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