Contents & References of Checking the performance of absorption refrigeration systems with 3 levels and 4 different temperature levels
List:
First chapter: Introduction
1- Introduction. 1
1-1- Absorption cooling system with water coolant and lithium bromide absorbent. 5
1-1-1- Single effect cycle 7
1-1-2- Double effect cycle 8
1-2- Absorption cooling system with ammonia refrigerant and water absorbent material. 12
Chapter Two: Thermodynamic model of the system. 21
2-1- Refrigeration systems with 3 irreversible heat sources. 21
2-2- Refrigeration systems with 4 irreversible heat sources 25
Chapter three: Evolutionary and genetic algorithms and their application in multi-objective optimization. 35
3-1- Introduction..35
3-2- Introduction of genetic algorithms. 41
3-2-1- Terms of genetic algorithm. 41
3-2-2-chromosomal display. 45
3-2-3- Initial population. 48
3-2-4- Fitness function and merit allocation. 50
3-2-5- Selection. 52
3-2-5-1-Selection space. 52
3-2-5-2- selection pressure. 53
3-2-5-3- selection method. 53
3-2-6- genetic operators. 56
3-3- Basic definitions and concepts in multi-objective optimization. 63
3-3-1- multi-objective optimization problem. 64
3-3-2- Possible space. 66
3-3-3- Relations between target vectors. 67
3-3-4 – Pareto dominance. 68
3-3-5- Pareto optimality
Chapter one: Introduction
1- Introduction. 1
1-1- Absorption cooling system with water coolant and lithium bromide absorbent. 5
1-1-1- Single effect cycle 7
1-1-2- Double effect cycle 8
1-2- Absorption cooling system with ammonia refrigerant and water absorbent material. 12
Chapter Two: Thermodynamic model of the system. 21
2-1- Refrigeration systems with 3 irreversible heat sources. 21
2-2- Refrigeration systems with 4 irreversible heat sources 25
Chapter three: Evolutionary and genetic algorithms and their application in multi-objective optimization. 35
3-1- Introduction..35
3-2- Introduction of genetic algorithms. 41
3-2-1- Terms of genetic algorithm. 41
3-2-2-chromosomal display. 45
3-2-3- Initial population. 48
3-2-4- Fitness function and merit allocation. 50
3-2-5- Selection. 52
3-2-5-1-Selection space. 52
3-2-5-2- selection pressure. 53
3-2-5-3- selection method. 53
3-2-6- genetic operators. 56
3-3- Basic definitions and concepts in multi-objective optimization. 63
3-3-1- multi-objective optimization problem. 64
3-3-2- Possible space. 66
3-3-3- Relations between target vectors. 67
3-3-4 – Pareto dominance. 68
3-3-5- Pareto optimality. 70
3-3-6- Pareto optimal set and front and ideal point. 71
3-3-7- Balance. 72
3-4- Multi-objective optimization using genetic algorithm. 73
3-4-1-Comparison of presented methods and algorithms 76
3-4-2- Genetic algorithm for sorting non-dominant answers improved NSGA II 78
Chapter four: optimization results
4-1- The first scenario. 88
4-2- The second scenario. 92
4-3- The third scenario. 99
Chapter five: conclusions and suggestions. 106
5-1- Conclusion and suggestions. 106
References.
Source:
[1] Yan, Z: Comment on "Ecological optimization criterion for finite-time heat-engines". J. Appl. Phys. 73(7), 3583 (1993)
[2] Bhardway, PK, Kaushik, SC, Jain, S: Finite time optimization of an endoreversible and irreversible vapor absorption refrigeration system. Energy Converse. Manage. 44(7), 1131–1144 (2003)
[3] Bhardway, PK, Kaushik, SC, Jain, S: General performance characteristics of an irreversible vapor absorption refrigeration system using finite time thermodynamic approach. Int. J. Ther. Sci. 44(2), 189–196 (2005)
[4] Chen, J: The optimum performance characteristics of a four-temperature-level irreversible absorption refrigerator at maximum specific cooling load. J.Phys.D: Appl. Phys. 32(23), 3085–3910 (1999)
[5] Chen, J: Optimal performance analysis of irreversible cycles used as heat pumps and refrigerators. J. Phys. D:Appl. Phys. 30(4), 582–587 (1997)
[6] Sun, F, Qin, X, Chen, L, Wu, C: Optimization between heating load and30(4), 582–587 (1997)
[6] Sun, F, Qin, X, Chen, L, Wu, C: Optimization between heating load and entropy-production rate for endoreversible absorption heat-transformers. Appl. Energy. 81(4), 434–448 (2005)
[7] Chen, L, Zheng, T, Sun, F, Wu, C: Irreversible four-temperature-level absorption refrigerator. Sol. Energy 80(3), 347–360 (2006)
[8] Ngouateu Wouagfack, P.A., Tchinda, R., 2011a. Performance optimization of three-heat-source irreversible refrigerators based on a new thermo-ecological criterion. Int. J. Refrigeration 34, 1008-1015.
[9] Ngouateu Wouagfack, P.A., Tchinda, R., 2011b. Irreversible three heat-source refrigerator with heat transfer law of and its performance optimization based on ECOP criterion. Energy Syst. 2, 359-376.
[10] Ngouateu Wouagfack, P.A., Tchinda, R., 2013. Finite-time thermodynamics optimization of absorption refrigeration systems: a review. Renew. Sust. Energy Rev. 21, 524-536.
[11] E. Acikkalp, Modified thermo-ecological optimization for refrigeration systems and an application for irreversible four-temperature-level absorption refrigerator, International Journal of Energy and Environmental Engineering 2013, 4(20) 1-9.
[12] Haimes Y. Y., Tarvainen K., Shima T., Thadathil J. "On a bi-criterion formulation of the problems of integrated system identification and system optimization", IEEE Transactions on Systems, Man and Cybernetics, Vol. 1, pp. 296–297, 1971.
[13] Lin J. “Multiple-objective problems: Pareto-optimal solutions by method of proper equality constraints”, IEEE Transactions on Automatic Control, Vol. 21, No. 5, pp. 641–650, 1976.
[14] Whitley D. “An overview of evolutionary algorithms: practical issues and common pitfalls”, Information and Software Technology Vol. 43, No. 15, pp. 817–831, 2001.
[15] Holland J.H. “Adaptation in Natural and Artificial Systems”, University of Michigan Press, Ann Arbor, 1975.
[16] Kirkpatrick S., Gelatt Jr. C. D., Vecchi M. P. "Optimization by simulated annealing" Science, vol. 220, No. 4598, pp. 671–680, 1983.
[17] Toffolo A., Lazzaretto A. "Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design", Energy, Vol. 27, pp. 549–567, 2002.
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