IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v65y2016icp676-684.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032116303574
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2016.07.029?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    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. 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.
    3. 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.
    4. Vicha, T. & Dohnal, M., 2008. "Qualitative identification of chaotic systems behaviours," Chaos, Solitons & Fractals, Elsevier, vol. 38(1), pages 70-78.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dohnal, Mirko & Doubravsky, Karel, 2016. "Equationless and equation-based trend models of prohibitively complex technological and related forecasts," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 297-304.
    2. Thomas Sachs & Anna Gründler & Milos Rusic & Gilbert Fridgen, 2019. "Framing Microgrid Design from a Business and Information Systems Engineering Perspective," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(6), pages 729-744, December.
    3. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    4. Doubravsky, Karel & Dohnal, Mirko, 2018. "Qualitative equationless macroeconomic models as generators of all possible forecasts based on three trend values—Increasing, constant, decreasing," Structural Change and Economic Dynamics, Elsevier, vol. 45(C), pages 30-36.
    5. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, vol. 9(8), pages 1-34, August.
    6. Weng, Yuwei & Chang, Shiyan & Cai, Wenjia & Wang, Can, 2019. "Exploring the impacts of biofuel expansion on land use change and food security based on a land explicit CGE model: A case study of China," Applied Energy, Elsevier, vol. 236(C), pages 514-525.
    7. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    8. Jang Ho Kim & Yongjae Lee & Woo Chang Kim & Frank J. Fabozzi, 2022. "Goal-based investing based on multi-stage robust portfolio optimization," Annals of Operations Research, Springer, vol. 313(2), pages 1141-1158, June.
    9. Tsionas, Mike G., 2019. "Multi-objective optimization using statistical models," European Journal of Operational Research, Elsevier, vol. 276(1), pages 364-378.
    10. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    11. Pätäri, Eero & Karell, Ville & Luukka, Pasi & Yeomans, Julian S, 2018. "Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence," European Journal of Operational Research, Elsevier, vol. 265(2), pages 655-672.
    12. Carayannis, Elias G. & Ferreira, Fernando A.F. & Bento, Paulo & Ferreira, João J.M. & Jalali, Marjan S. & Fernandes, Bernardo M.Q., 2018. "Developing a socio-technical evaluation index for tourist destination competitiveness using cognitive mapping and MCDA," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 147-158.
    13. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    14. Peura, Pekka, 2013. "From Malthus to sustainable energy—Theoretical orientations to reforming the energy sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 309-327.
    15. Gordon, Joel A. & Balta-Ozkan, Nazmiye & Nabavi, Seyed Ali, 2022. "Homes of the future: Unpacking public perceptions to power the domestic hydrogen transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    16. Qi, Yue & Liao, Kezhi & Liu, Tongyang & Zhang, Yu, 2022. "Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths," Operations Research Perspectives, Elsevier, vol. 9(C).
    17. Cao, Yan & Doustgani, Amir & Salehi, Abozar & Nemati, Mohammad & Ghasemi, Amir & Koohshekan, Omid, 2020. "The economic evaluation of establishing a plant for producing biodiesel from edible oil wastes in oil-rich countries: Case study Iran," Energy, Elsevier, vol. 213(C).
    18. Max Berre, 2022. "Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?," Post-Print hal-03829877, HAL.
    19. Julio Cezar Soares Silva & Diogo Ferreira de Lima Silva & Luciano Ferreira & Adiel Teixeira de Almeida-Filho, 2022. "A dominance-based rough set approach applied to evaluate the credit risk of sovereign bonds," 4OR, Springer, vol. 20(1), pages 139-164, March.
    20. Kallio, Markku & Halme, Merja & Dehghan Hardoroudi, Nasim & Aspara, Jaakko, 2022. "Transparent structured products for retail investors," European Journal of Operational Research, Elsevier, vol. 302(2), pages 752-767.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:65:y:2016:i:c:p:676-684. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.