Location optimization of agricultural residues-based biomass plant using Z-number DEA

Authors

  • Delaram Heydarian School of Industrial Engineering, College of Engineering, University of Tehran, Iran
  • Fariborz Jolai School of Industrial Engineering, College of Engineering, University of Tehran, Iran
  • Omid Momeni School of Industrial Engineering, College of Engineering, University of Tehran, Iran
  • Reza Yazdanparast School of Industrial Engineering, College of Engineering, University of Tehran, Iran
Abstract:

Co-firing biomass plants are of extensive demand due to utilization of both agricultural residues (main) and natural gas (stand-by). Researchers have shown that one strategic decision in establishment of agricultural residues based plants, is location optimization problem. Moreover, mismatch between agricultural lands and biomass plants can lead to high transportation costs and related carbon dioxide emissions. Standard indicators are considered and used for the stated multi-objective mathematical problem. This article presents a novel approach based on Z-number data envelopment analysis (DEA) model to handle severe uncertainty associated with actual data. The multi-objective mathematical model considers environmental, economic and social aspects of biomass plants. Moreover, fuzzy DEA model is utilized to verify and validate the results of Z-number DEA model through 30 independent experiments. The obtained results indicate that “accessibility to water”, “population”, “cost of land”, and “unemployment rate” are the most significant factors in location optimization of co-firing power plants. The obtained results also indicate that “Ilam”, “Semnan”, “Kohgiluyeh and Boyer-ahmad”, “South Khorasan”, and “Chaharmahal and Bakhttiari” are the optimum locations. This is the first unique approach for location optimization of co-firing plants based on combined agricultural residues and natural gas under uncertainty. Second, a unique fuzzy mathematical optimization approach is presented. Third, it is a practical approach for biomass power plants.

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Journal title

volume 12  issue 1

pages  39- 65

publication date 2018-12-26

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