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Chapter 6: Results and discussion 6.1 Introduction In order to enhance the understanding of the system's performance, it is important to use several analyses to see how this performance varies with design parameters. In this chapter, the results of thermodynamic modeling, exergy, economic and environmental impact assessment, and optimization are explained. Exergy analysis can help develop strategies and guidelines for more efficient and effective use of energy, and is utilized to study various thermal processes, especially power generation, CHP, trigeneration and multigeneration. The exergy analysis includes the determination of the exergy destruction rate and exergy efficiency of each component in the system and also determines the overall exergy efficiency of the multigeneration system. Exergy analysis also helps to identify and quantify the source of irreversibilities in the systems that are associated with each component. Economic analysis shows the total cost rate of the system, cost of each component, cost of electricity and cost of environmental impacts. The environmental impact assessment shows how much reduction in greenhouse gases is possible when shifting from conventional power generation units to CHPs and multigeneration energy systems. Further improvement of a thermal system can be obtained by using optimization. Since both efficiency and total cost rate of the systems are important, single objective optimization will not yield a true optimum. Therefore, a multi-objective optimization should be applied to the system. In this chapter, different output key parameters are investigated for three different systems. These parameters are overall exergy efficiency, total exergy destruction rate, cooling and heating load, hydrogen production rate, fresh water mass flow rate, CO2 emission of the system and net power output of each system. Also, to enhance the understanding of the system performance, a comprehensive parametric study is conducted to see the result of variation in several major design parameters of system performance. Finally, by defining some objective functions, the optimum design parameters are obtained using a multi-objective genetic algorithm optimization technique. 114PDF Image | Energy Systems for Multigeneration Purposes
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