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

Renewable and Sustainable Energy Reviews

Volumn 65, Issue , 2016, Pages 676-684

  Dohnal, Mirko ^a  

^a BRNO UNIVERSITY OF TECHNOLOGY   (Czech Republic)

Author keywords

Biofuel; Complex biofuel related model; Qualitative model; Qualitative reasoning

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION MAKING; ECONOMIC AND SOCIAL EFFECTS; TAXATION;

BROAD SPECTRUM; COMPLEX BIOFUEL RELATED MODEL; COMPLEX DECISION; DECISIONS MAKINGS; EXPERT KNOWLEDGE; MODEL-BASED OPC; OBJECTIVE FUNCTIONS; QUALITATIVE MODELING; QUALITATIVE REASONING; TRADE OFF;

BIOFUELS;

EID: 84989837130     PISSN: 13640321     EISSN: 18790690     Source Type: Journal    
DOI: 10.1016/j.rser.2016.07.029     Document Type: Review

References (79)

1. Havlik, P., Schneider, U.A., Schmid, E., Bottcher, H., Fritz, S., Skalsky, R., Aoki, K., De Cara, S., Kindermann, G., Kraxner, F., Leduc, S., McCallum, I., Mosnier, A., Sauer, T., Obersteiner, M., Global land-use implications of first and second generation biofuel targets. Energy Policy, 2011, 39 10.
2. Lu, W., Pedrycz, W., Liu, X., Yang, J., Li, P., The modeling of time series based on fuzzy information granules. Expert Syst Appl, 2014, 41–48.
3. Baah, C., Dohnal, M., Qualitative reasoning with reference to polymers. Mater Des, 1995, 16 2.
4. Dohnal, M., Qualitative partial differential equations and their realistic applications. Comp Ind, 1992, 20–22.
5. Patra, T.K., Sheth, P.N., Biomass gasification models for down draft gasifier: a state-of-the-art review. Renew Sust Energy Rev, 2015, 50.
6. Affuso, E., Hite, D., A model for sustainable land use in biofuel production: an application to the state of Alabama. Energy Econ, 2013, 37.
7. Ribeiro, B.E., Quintanilla, M.A., Transitions in biofuel technologies: an appraisal of the social impacts of cellulosic ethanol using the Delphi method. Technol Forecast Soc Change, 2015, 92.
8. Ji, L.Q., An assessment of agricultural residue resources for liquid biofuel production in China. Renew Sust Energy Rev, 2015, 44.
9. Parsons, S., Dohnal, M., Qualitative, semiqualitative and interval algebras, and their application to engineering problems. Eng Appl Artif Intell, 1992, 5–6.
10. Parsons, S., Kubat, M., Dohnal, M., A rough set approach to reasoning under uncertainty. J Exp Theor Artif Intell, 1995, 7 2.
11. Parsons, S., Dohnal, M., The qualitative and semiqualitative analysis of environmental problems. Environ Softw, 1995, 10–12.
12. Orrella, D., McSharry, P., System economics: overcoming the pitfalls of forecasting models via a multidisciplinary approach. Int J Forecast, 2009, 25 4.
13. Zopounidis, C., Galariotis, E., Doumpos, M., Sarri, S., Andriosopoulos, K., Multiple criteria decision aiding for finance: an updated bibliographic survey. Eur J Oper Res, 2015, 247 2.
14. Iskin, I., Daim, T., Kayakutlu, G., Altuntas, M., Exploring renewable energy pricing with analytic network process — comparing a developed and a developing economy. Energy Econ, 2012, 34 34.
15. Lipmann, O., Bogen, H., Naive Physik: Arbeiten aus dem Instuut Fur angewandte Psychologie in Berlin; Theoretische und experimentele Untersuchungen fuer Die Feihigkeit zu intelligentem Handeln. 1923, Johann Ambrosius Barth, Leipzig.
16. Bredeweg, B., Linnebank, F., Bouwer, A., Liem, J., Garp3-Workbench for qualitative modelling and simulation. Ecol Inform, 2009, 4–5.
17. Michlika, L.A., Luchta, W., Bondeau, A., Beringer, T., Integrated assessment of sustainability tradeoffs. Renew Sust Energy Rev, 2011, 15–16.
18. Ahmad, A.L., Yasin, N.H.M., Derek, C.J.C., Lim, J.K., Microalgae as a sustainable energy source for biodiesel production: a review. Renew Sust Energy Rev, 2011, 15 1.
19. Saxena, R.C., Adhikari, D.K., Goyal, H.B., Biomass-based energy fuel through biochemical routes: a review. Renew Sust Energy Rev, 2009, 13 1.
20. Raman, S., Mohr, A., Helliwell, R., Ribeiro, B., Shortall, O., Smith, R., Millar, K., Integrating social and value dimensions into sustainability assessment of lignocellulosic biofuels. Biomass Bioenergy, 2015, 82 (November).
21. Su, Y., Zhang, P., Su, Y., An overview of biofuels policies and industrialization in the major biofuel producing countries. Renew Sust Energy Rev, 2015, 50 10.
22. Bryman, A., Social research methods. 4th ed., 2012, Oxford University Press, Oxford.
23. Findlater, K.M., Kandlikar, M., Land use and second-generation biofuel feedstocks: the unconsidered impacts of Jatropha biodiesel in Rajasthan, India. Energy Policy, 2011, 39 6.
24. Alic, J.A., Biofuel battles: Politics, policy, and the pentagon. Energy Res Soc Sci, 10, 2015 (November).
25. Yuan, F.C.H., Lee, C.H.H., Using least square support vector regression with genetic algorithm to forecast beta systematic risk. J Comput Sci, 2015, 11 (November).
26. Chin, H.C.H., Choong, W.W., Alwi, S.R.W., Mohammed, A.H., Issues of social acceptance on biofuel development. J Clean Prod, 2014, 71 (May).
27. Wüstenhagen, R., Wolsink, M., Burer, M.J., Social acceptance of renewable energy innovation: an introduction to the concept. Energy Policy, 2007, 35 35.
28. Sauter, R., Watson, J., Strategies for the deployment of micro-generation: implications for social acceptance. Energy Policy, 2007, 35 35.
29. Russell, S., Norvig, P., Artificial intelligence: a modern approach. 1995, Prentice-Hall, Englewood Cliffs.
30. Fiordaliso, A., A nonlinear forecasts combination method based on Takagi–Sugeno fuzzy systems. Int J Forecast, 1998, 14 3.
31. Suganthi, L., Iniyan, S., Samuel, A.A., Applications of fuzzy logic in renewable energy systems – a review. Renew Sust Energy Rev, 2015, 48 48.
32. Dohnal, M., A methodology for common-sense model development. Comput Ind, 1991, 16 2.
33. Konecny, J., Vicha, T., Dohnal, M., Qualitative phase portrait of modified Black–Scholes model. Exp Syst Appl, 2010, 37 5.
34. Struss, P., Problems of interval-based qualitative reasoning. 1990, Morgan Kaufmann Publishers, San Francisco.
35. Dohnal, M., Fraser, D.M., Kerkovsky, M., A fuzzy pooling of investment cost knowledge. Int J Prod Econ 43 (1996), 2–3.
36. Dohnal, M., Starzak, M., Kerkovsky, M., Dohnalova, J., Vystrcil, J., Koivisto, R., Pokorny, M., Vanis, M., Parsons, S., A fuzzy upgrading of integrated vague managerial and engineering knowledge. Int J Prod Econ, 1993, 32 32.
37. Doubravsky, K., Dohnal, M., Reconciliation of decision-making heuristics based on decision trees topologies and incomplete fuzzy probabilities sets. PLoS One, 2015, 10–17.
38. Serra, T., Time-series econometric analyses of biofuel-related price volatility. Agric Econ, 2013, 44 s1.
39. Chatterjee, S., Sethi, M.R., Asad, M.W.A., Production phase and ultimate pit limit design under commodity price uncertainty. Eur J Oper Res, 2016, 248 2.
40. Gardebroek, C., Hernandez, M.A., Robles, M., Market interdependence and volatility transmission among major crops. Agric Econ, 2015, 46 5.
41. Iqbal, M., Azam, M., Naeem, M., Khwaja, A.S., Anpalagan, A., Optimization classification, algorithms and tools for renewable energy: a review. Renew Sust Energy Rev, 2014, 39 11.
42. Chen, J., Gomez, J.A., Höffner, K., Barton, P.I., Henson, M.A., Metabolic modeling of synthesis gas fermentation in bubble column reactors. Biotechnol Biofuels, 2015, 8–89.
43. Aghabozorgi, S., Seyed Shirkhorshidi, A., Ying Wah, T., Time-series clustering – a decade review. Inf Syst, 53, 2015 (October–November).
44. Villez, K., Rosén, C., Anctil, F., Duchesne, C., Vanrolleghem, P.A., Qualitative Representation of Trends (QRT): extended method for identification of consecutive inflection points. Comp Chem Eng, 2013, 48 1.
45. Villez, K., Venkatasubramanian, V., Rengaswamy, R., Generalized shape constrained spline fitting for qualitative analysis of trends. Comp Chem Eng, 2013, 58 11.
46. Harte, J., Newman, E.A., Maximum information entropy: a foundation for ecological theory. Trends Ecol Evol, 2014, 29 7.
47. Mellit, A., Kalogirou, S.A., Hontoria, L., Shaari, S., Artificial intelligence techniques for sizing photovoltaic systems: a review. Renew Sust Energy Rev, 2009, 13 2.
48. Wong, K.I., Vong, C.H.M., Wong, P.K., Luo, J., Sparse Bayesian extreme learning machine and its application to biofuel engine performance prediction. Neurocomputing, 2015, 149 (February).
49. Raiman, O., Order of magnitude reasoning. Artif Intell, 1991, 51 51.
50. Xiao, Z., Gong, K., Zou, Y., A combined forecasting approach based on fuzzy soft sets. J Comput Appl Math, 2009, 228 1.
51. Yue, D., You, F., Snyder, S.W., Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Comp Chem Eng, 2014, 66–67.
52. Mostafaee, A., Hladik, M., Cerny, M., Inverse linear programming with interval coefficients. J Comput Appl Math, 2016, 292 1.
53. Kim, J.S., Edgar, T.F., Optimal scheduling of combined heat and power plants using mixed-integer nonlinear programming. Energy, 2014, 77 (December).
54. D'Ambrosio, C., Lodi, A., Wiese, S., Bragalli, C., Mathematical programming techniques in water network optimization. Eur J Oper Res, 2015, 243 243.
55. Sahebi, H., Nickel, S., Ashayeri, J., Strategic and tactical mathematical programming models within the crude oil supply chain context—a review. Comp Chem Eng, 2014, 68–69.
56. Atalay, K.D., Eraslan, E., Çinar, M.O., A hybrid algorithm based on fuzzy linear regression analysis by quadratic programming for time estimation: an experimental study in manufacturing industry. J Manuf Syst, 2015, 36 (July).
57. Chen, L., Xu, Z., A new fuzzy programming method to derive the priority vector from an interval reciprocal comparison matrix. Info Sci, 2015, 316–320 (September).
58. Liu, H., Tian, H., Liang, X., Li, Y., Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Appl Energy, 2015, 157 (November).
59. Liu, H., Tian, H., Liang, X., Li, Y., New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, mind evolutionary algorithm and artificial neural networks. Renew Energy, 2015, 83 (November).
60. Sermpinis, G., Stasinakis, C.H., Theofilatos, K., Karathanasopoulos, A., Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—support vector regression forecast combinations. EUR J Oper Res, 2015, 247 3.
61. Raza, M.Q., Khosravi, A., A review on artificial intelligence based load demand forecasting techniques for Smart Grid and buildings. Renew Sust Energy Rev, 2015, 50 10.
62. Magnus, J.R., Katyshev, P.K., Peresetsky, A.A., Econometrics: a first course. 2007, Delo Publishers, Moscow.
63. Haettenschwiler, P., Neues anwenderfreundliches Konzept der Entscheidungs-unterstutzung. Gutes Entscheiden in Wirtschaft Politik und Gesellschaft. 1999, Hochschulverlag AG, Zurich.
64. Power DJ. A Brief History of Decision Support Systems. DSSResources.COM. version 4.0, March 10. Available at: http://DSSResources.COM/history/ dsshistory.html); 2007 [accessed 10.03.14].
65. Bukharov, O.E., Bogolyubov, D.P., Development of a decision support system based on neural networks and a genetic algorithm. Expert Syst Appl 42 (2015), 15–16.
66. Yuan, S., Zhou, X., Chen, R., Song, B., Study on modelling microalgae growth in nitrogen-limited culture system for estimating biomass productivity. Renew Sust Energy Rev, 2014, 34–36.
67. Bahela, E., Marrouch, W., Gaudet, G., The economics of oil, biofuel and food commodities. Resour Energy Econ, 2013, 35 4.
68. Wright, G., Goodwin, P., Decision making and planning under low levels of predictability: enhancing the scenario method. Int J Forecast, 2009, 25 4.
69. Lee, W.Y., Goodwin, P., Fildes, R., Nikolopoulos, K., Lawrence, M., Providing support for the use of analogies in demand forecasting tasks. Int J Forecast, 2007, 23 23.
70. Dohnal, M., Naive models as active expert system in bioengineering and chemical engineering. Collect Czech Chem Commun, 1988, 53–57.
71. Smith, B., Casati, R., Naive physics. Philos Psychol, 1994, 7 2.
72. Hayes, P.J., The second naive physics manifesto. Hobbs, J., Moore, R., (eds.) Formal theories of the commonsense world, 1985, Ablex Publishing, Norwood.
73. Hayes, P.J., Naive physics 1: ontology for liquids. Hobbs, J., Moore, R., (eds.) Formal theories of the commonsense world, 1985, Ablex Publishing, Norwood.
74. Mueller, E.T., Commonsense reasoning. 2015, Morgan Kaufmann Publishers, San Francisco.
75. Hamscher, W., Kiang, M.Y., Lang, R., Qualitative reasoning in business, finance, and economics: Introduction. Decis Support Syst, 1995, 15 2.
76. De Kleer, J., Brown, J., A qualitative physics based on confluences. Artif Intel, 1984, 24 1.
77. Bredeweg, B., Salles, P., Bouwer, A., Liem, J., Nuttle, T., Cioaca, E., Nakova, E., Noble, R., Caldas, A., Uzunov, Y., Varadinova, E., Zitek, A., Towards a structured approach to building qualitative reasoning models and simulations. Ecol Inform, 2008, 3 1.
78. Vicha, T., Dohnal, M., Qualitative identification of chaotic systems behaviours. Chaos Soliton Fract, 2008, 38 1.
79. Franco, C.J., Zapata, S., Dyner, I., Simulation for assessing the liberalization of biofuels. Renew Sust Energy Rev, 2015, 41 41.