Estimating Petroleum Exergy Production and Consumption Using a Simulated Annealing Approach

This study deals with the development of the petroleum exergy production and consumption relations in order to better analyze exergy values and predict the future projections using the simulated annealing (SA) approach, which is a powerful technique used to solve many optimization problems. The exergy estimation is performed based on the indicators of gross domestic product (GDP) and the percentage of vehicle ownership figures in Turkey, which is given as an illustrative example. The so-called SA exergy production and consumption (SAPEX) model is developed, while the exergy values obtained using the SAPEX model are also compared with those using the genetic algorithm (GA) approach. It is determined that the SAPEX model developed predicts the exergy values better than the GA model. It may be concluded that the models proposed here can be used as an alternative solution and estimation technique to available estimation techniques in predicting the future energy and exergy utilization values of countries. This study is also expected to give a new direction to engineers, scientists, and policy makers in implementing energy planning studies and in dictating the energy strategies as a potential tool.

[1]  M. F. Cardoso,et al.  The simplex-simulated annealing approach to continuous non-linear optimization , 1996 .

[2]  Soner Haldenbilen,et al.  Genetic algorithm approach to estimate transport energy demand in Turkey , 2005 .

[3]  Arif Hepbasli,et al.  An Application of Genetic Algorithm Search Techniques to the Future Total Exergy Input/Output Estimation , 2006 .

[4]  Pedro M. Vilarinho,et al.  A simulated annealing approach for manufacturing cell formation with multiple identical machines , 2003, Eur. J. Oper. Res..

[5]  Ibrahim Dincer,et al.  Energy Reality and Future Projections for Canada , 1997 .

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Peter T. Cummings,et al.  Process optimization via simulated annealing: Application to network design , 1989 .

[8]  Ibrahim Dincer,et al.  Energy intensities for Canada , 1996 .

[9]  M. E. El-Hawary,et al.  An innovative simulated annealing approach to the long-term hydroscheduling problem , 2003 .

[10]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[11]  Serge Domenech,et al.  Synthesis of heat‐exchanger network by simulated annealing and NLP procedures , 1997 .

[12]  Lefteris Angelis,et al.  A simulated annealing approach for multimedia data placement , 2004, J. Syst. Softw..

[13]  I. Dincer The role of exergy in energy policy making , 2002 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Arif Hepbasli,et al.  Estimating petroleum exergy production and consumption using vehicle ownership and GDP based on genetic algorithm approach , 2004 .

[16]  Ehl Emile Aarts,et al.  Statistical cooling : a general approach to combinatorial optimization problems , 1985 .

[17]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[18]  Arif Hepbasli,et al.  Estimating Energy and Exergy Production and Consumption Values Using Three Different Genetic Algorithm Approaches. Part 2: Application and Scenarios , 2005 .