Thermoenvironomic optimization of gas turbine cycles with air preheat

In optimization of thermodynamic cycles, it is appropriate for the economic and environmental aspects to be considered simultaneously. The sum of the fuel and investment cost flowrates is known as thermoeconomic objective function. A new objective function, known as a thermoenvironomic objective function, is obtained by integrating the environmental impacts, which should be defined and expressed in terms of cost, and thermoeconomic objective function. The objective of this paper is to study the effect of air preheater (APH) in the thermodynamic cycles by considering environmental impacts. In order to perform this, four simple thermodynamic cycles are selected and economic and environmental aspects are optimized. All considered thermodynamic cycles produce 30 MW of electricity and two of them, which are cogeneration systems, produce 18 kg/s of saturated steam at 20 bar, too. For optimization of these cycles, a code has been developed in MATLAB based on the real coding and optimal solutions have been obtained. The results show that the existence of APH increases exergetic efficiency of the cycles and the environmental impacts cost flowrate but decreases the total cost flowrate. Also, in the cycles without APH, the optimum values of decision variables corresponding to thermoeconomic and thermoenvironomic objective functions do not change considerably, while in the cycles with APH, they change noticeably.

[1]  Antonio Valero,et al.  CGAM Problem: Definition and Conventional Solution , 1994 .

[2]  Andrzej Osyczka,et al.  Evolutionary Algorithms for Single and Multicriteria Design Optimization , 2001 .

[3]  José Luz Silveira,et al.  Thermoeconomic analysis method for optimization of combined heat and power systems , 2000 .

[4]  Andrea Toffolo,et al.  Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design , 2004 .

[5]  Hoseyn Sayyaadi,et al.  Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system , 2009 .

[6]  N. K. Rizk,et al.  Semianalytical Correlations for NOx, CO, and UHC Emissions , 1993 .

[7]  Antonio Valero,et al.  Application of the exergetic cost theory to the CGAM problem , 1994 .

[8]  Ö. Gülder Flame Temperature Estimation of Conventional and Future Jet Fuels , 1986 .

[9]  Hong-Tzer Yang,et al.  Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions , 1996 .

[10]  Christos A. Frangopoulos,et al.  Application of the thermoeconomic functional approach to the CGAM problem , 1994 .

[11]  A. Fichera,et al.  Using Genetic Algorithms and the Exergonomic Approach to Optimize District Heating Networks , 1998 .

[12]  Manuel E. Cruz,et al.  Integration of an iterative methodology for exergoeconomic improvement of thermal systems with a process simulator , 2004 .

[13]  Manuel E. Cruz,et al.  INTEGRATION OF A MATHEMATICAL EXERGOECONOMIC OPTIMIZATION PROCEDURE WITH A PROCESS SIMULATOR: APPLICATION TO THE CGAM SYSTEM , 2005 .

[14]  George Tsatsaronis,et al.  Exergoeconomic evaluation and optimization of energy systems — application to the CGAM problem , 1994 .

[15]  Michael von Spakovsky,et al.  Application of Engineering Functional Analysis to the Analysis and Optimization of the CGAM Problem , 1994 .

[16]  Andrea Toffolo,et al.  Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design , 2002 .

[17]  George Tsatsaronis,et al.  Exergy-aided cost minimization , 1997 .

[18]  Pradeep K. Sahoo,et al.  Exergoeconomic analysis and optimization of a cogeneration system using evolutionary programming , 2008 .

[19]  Seyyed Masoud Seyyedi,et al.  A new iterative approach to the optimization of thermal energy systems: Application to the regenerative Brayton cycle , 2010 .

[20]  Luigi Marletta,et al.  A Comparison of Methods for Optimizing Air-Conditioning Systems According to the Exergonomic Approach , 2001 .