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Complex biofuels related scenarios generated by qualitative reasoning under severe information shortages: A review

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  • Dohnal, Mirko

Abstract

There is a broad spectrum of BR (Biofuel Related) models. However the models are highly specific. Such models cannot flexibly support complex decision making activities to identify feasible trade-offs among many objective functions. They represent just one point of view; e.g. engineering, company economics. Therefore CBRMs (Complex Biofuel Related Model) are studied in this paper. CBRMs must cover additional aspects, e.g. macroeconomic, ecology, sociology and politics. Such models are inevitably unique, partially subjective, inconsistent, vague and multidimensional. AI (Artificial Intelligence) has developed a number of tools to solve such problems. Qualitative reasoning is one of them. It is based on the least information intensive quantifiers i.e. trends. There are just three trend / qualitative values: plus/increasing; zero/constant; negative/decreasing. There are many CBRM items related to soft aspects e.g. politics, taxation etc. which cannot be quantified by numbers. They are too vague. For example – if the political/sociological sensitivity of land ownership is increasing then the cost of biofuels is increasing. Such verbal knowledge item cannot be incorporated into a traditional numerical model. The paper describes developments of CBRMs based on expert knowledge and common sense. A consensus among experts is often not reached because of substantial subjectivity of experts’ knowledge. A qualitative intersection of CBRMs developed by several experts is simple. The case study presents a model based on integration of 10 heuristics using 9 variables e.g. Refinery Capacity, Cost of Biofuel Production, and Biofuel Demand. The result is represented by 13 scenarios. The paper is self-contained, no a prior knowledge of qualitative models is required.

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  • Dohnal, Mirko, 2016. "Complex biofuels related scenarios generated by qualitative reasoning under severe information shortages: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 676-684.
    • Handle: RePEc:eee:rensus:v:65:y:2016:i:c:p:676-684
    DOI: 10.1016/j.rser.2016.07.029
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    as
    1. Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
    2. Bahel, Eric & Marrouch, Walid & Gaudet, Gérard, 2013. "The economics of oil, biofuel and food commodities," Resource and Energy Economics, Elsevier, vol. 35(4), pages 599-617.
    3. Teresa Serra, 2013. "Time-series econometric analyses of biofuel-related price volatility," Agricultural Economics, International Association of Agricultural Economists, vol. 44(s1), pages 53-62, November.
    4. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.
    5. Vicha, T. & Dohnal, M., 2008. "Qualitative identification of chaotic systems behaviours," Chaos, Solitons & Fractals, Elsevier, vol. 38(1), pages 70-78.
    6. Franco, Carlos J. & Zapata, Sebastian & Dyner, Isaac, 2015. "Simulation for assessing the liberalization of biofuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 298-307.
    7. Iskin, Ibrahim & Daim, Tugrul & Kayakutlu, Gulgun & Altuntas, Mehmet, 2012. "Exploring renewable energy pricing with analytic network process — Comparing a developed and a developing economy," Energy Economics, Elsevier, vol. 34(4), pages 882-891.
    8. Wustenhagen, Rolf & Wolsink, Maarten & Burer, Mary Jean, 2007. "Social acceptance of renewable energy innovation: An introduction to the concept," Energy Policy, Elsevier, vol. 35(5), pages 2683-2691, May.
    9. Ribeiro, Barbara E. & Quintanilla, Miguel A., 2015. "Transitions in biofuel technologies: An appraisal of the social impacts of cellulosic ethanol using the Delphi method," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 53-68.
    10. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    11. D’Ambrosio, Claudia & Lodi, Andrea & Wiese, Sven & Bragalli, Cristiana, 2015. "Mathematical programming techniques in water network optimization," European Journal of Operational Research, Elsevier, vol. 243(3), pages 774-788.
    12. Fiordaliso, Antonio, 1998. "A nonlinear forecasts combination method based on Takagi-Sugeno fuzzy systems," International Journal of Forecasting, Elsevier, vol. 14(3), pages 367-379, September.
    13. Sauter, Raphael & Watson, Jim, 2007. "Strategies for the deployment of micro-generation: Implications for social acceptance," Energy Policy, Elsevier, vol. 35(5), pages 2770-2779, May.
    14. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
    15. Patra, Tapas Kumar & Sheth, Pratik N., 2015. "Biomass gasification models for downdraft gasifier: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 583-593.
    16. Karel Doubravsky & Mirko Dohnal, 2015. "Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-18, July.
    17. Chatterjee, Snehamoy & Sethi, Manas Ranjan & Asad, Mohammad Waqar Ali, 2016. "Production phase and ultimate pit limit design under commodity price uncertainty," European Journal of Operational Research, Elsevier, vol. 248(2), pages 658-667.
    18. Constantin Zopounidis & Emilios C. C Galariotis & Michael Doumpos & Stavroula Sarri & Kostas Andriosopoulos, 2015. "Multiple criteria decision aiding for finance: An updated bibliographic survey," Post-Print hal-02879842, HAL.
    19. Dohnal, M. & Starzak, M. & Kerkovsky, M. & Dohnalova, J. & Vystrcil, J. & Koivisto, R. & Pokorny, M. & Vanis, M. & Parsons, S., 1993. "A fuzzy upgrading of integrated vague managerial and engineering knowledge," International Journal of Production Economics, Elsevier, vol. 32(2), pages 209-228, September.
    20. Yuan, Shuo & Zhou, Xinping & Chen, Ranchi & Song, Bao, 2014. "Study on modelling microalgae growth in nitrogen-limited culture system for estimating biomass productivity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 525-535.
    21. Ahmad, A.L. & Yasin, N.H. Mat & Derek, C.J.C. & Lim, J.K., 2011. "Microalgae as a sustainable energy source for biodiesel production: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 584-593, January.
    22. Dohnal, M. & Fraser, D. M. & Kerkovsky, M., 1996. "A fuzzy pooling of investment cost knowledge," International Journal of Production Economics, Elsevier, vol. 43(2-3), pages 91-106, June.
    23. Findlater, K.M. & Kandlikar, M., 2011. "Land use and second-generation biofuel feedstocks: The unconsidered impacts of Jatropha biodiesel in Rajasthan, India," Energy Policy, Elsevier, vol. 39(6), pages 3404-3413, June.
    24. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    25. Ji, Li-Qun, 2015. "An assessment of agricultural residue resources for liquid biofuel production in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 561-575.
    26. Affuso, Ermanno & Hite, Diane, 2013. "A model for sustainable land use in biofuel production: An application to the state of Alabama," Energy Economics, Elsevier, vol. 37(C), pages 29-39.
    27. Saxena, R.C. & Adhikari, D.K. & Goyal, H.B., 2009. "Biomass-based energy fuel through biochemical routes: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 167-178, January.
    28. Kim, Jong Suk & Edgar, Thomas F., 2014. "Optimal scheduling of combined heat and power plants using mixed-integer nonlinear programming," Energy, Elsevier, vol. 77(C), pages 675-690.
    29. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    30. Liu, Hui & Tian, Hongqi & Liang, Xifeng & Li, Yanfei, 2015. "New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 83(C), pages 1066-1075.
    31. Orrell, David & McSharry, Patrick, 2009. "System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach," International Journal of Forecasting, Elsevier, vol. 25(4), pages 734-743, October.
    32. Havlík, Petr & Schneider, Uwe A. & Schmid, Erwin & Böttcher, Hannes & Fritz, Steffen & Skalský, Rastislav & Aoki, Kentaro & Cara, Stéphane De & Kindermann, Georg & Kraxner, Florian & Leduc, Sylvain & , 2011. "Global land-use implications of first and second generation biofuel targets," Energy Policy, Elsevier, vol. 39(10), pages 5690-5702, October.
    33. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    34. Constantin Zopounidis & Emilios Galariotis & Michael Doumpos & Stavroula Sarri & Kostas Andriosopoulos, 2015. "Multiple criteria decision aiding for finance: An updated bibliographic survey," Post-Print hal-01183389, HAL.
    35. Su, Yujie & Zhang, Peidong & Su, Yuqing, 2015. "An overview of biofuels policies and industrialization in the major biofuel producing countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 991-1003.
    36. Wright, George & Goodwin, Paul, 2009. "Decision making and planning under low levels of predictability: Enhancing the scenario method," International Journal of Forecasting, Elsevier, vol. 25(4), pages 813-825, October.
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    Cited by:

    1. Karel Doubravský & Alena Kocmanová & Mirko Dohnal, 2018. "Analysis of Sustainability Decision Trees Generated by Qualitative Models Based on Equationless Heuristics," Sustainability, MDPI, vol. 10(7), pages 1-18, July.

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