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A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

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  • Fausto Cavallaro

    (Department of Economics, Management, Society and Institutions (EGSI), University of Molise, Via De Sanctis, 86100 Campobasso, Italy)

Abstract

One aspect of the use of biomass for energy purposes which remains controversial concerns their full environmental sustainability. Considering the crucial importance of this problem, numerous authors have carried out evaluations of the environmental impact of the various types of biomass by means of several approaches. Although some of these methods are excellent environmental evaluation tools, they are unfortunately unable to manage uncertain input data. Instead, fuzzy-set based methods have proven to be able to deal with uncertainty in environmental topics. The original contributions proposed by fuzzy logic relate, on the one hand, to the representation of uncertain and vague information, and, on the other, to handling such information using fuzzy rules. A fuzzy inference system (FIS) constitutes the practice of framing mapping from the input to an output using fuzzy logic. In this paper, we propose an application of Takagi-Sugeno fuzzy inference modelling to build a synthetic index to assess the sustainability of production of the biomass for energy purposes.

Suggested Citation

  • Fausto Cavallaro, 2015. "A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass," Sustainability, MDPI, vol. 7(9), pages 1-13, September.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:9:p:12359-12371:d:55502
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    2. Mehdi Poornikoo & Kjell Ivar Øvergård, 2022. "Levels of automation in maritime autonomous surface ships (MASS): a fuzzy logic approach," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(2), pages 278-301, June.
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    4. C. J. Luis Pérez, 2020. "A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering," Mathematics, MDPI, vol. 8(9), pages 1-38, August.
    5. Garofalo, Pasquale & Mastrorilli, Marcello & Ventrella, Domenico & Vonella, Alessandro Vittorio & Campi, Pasquale, 2020. "Modelling the suitability of energy crops through a fuzzy-based system approach: The case of sugar beet in the bioethanol supply chain," Energy, Elsevier, vol. 196(C).
    6. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    7. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1, January.
    8. Omid Ghorbanzadeh & Hashem Rostamzadeh & Thomas Blaschke & Khalil Gholaminia & Jagannath Aryal, 2018. "A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 497-517, November.
    9. C. J. Luis Pérez, 2020. "Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes," Mathematics, MDPI, vol. 8(6), pages 1-26, June.
    10. Garofalo, Pasquale & Campi, Pasquale & Vonella, Alessandro Vittorio & Mastrorilli, Marcello, 2018. "Application of multi-metric analysis for the evaluation of energy performance and energy use efficiency of sweet sorghum in the bioethanol supply-chain: A fuzzy-based expert system approach," Applied Energy, Elsevier, vol. 220(C), pages 313-324.

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