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objectives. They used MATLAB multi-objective optimization algorithm of NSGA-II, which is based on the Genetic Algorithm, and obtained Pareto optimal frontier to find the Pareto optimal solutions. They compared the final optimal system with the base case and found that the exergetic efficiency in the multi-objective optimum design is 11.11% higher than that of the exergoeconomic optimized system, while the total product cost of the multi-objective optimal design is 16.7 higher than that of the exergoeconomic optimal system Ghaebi et al. [66] conducted the exergoeconomic optimization of a trigeneration system for heating, cooling and power production purpose based on total revenue requirement (TRR) method and using evolutionary algorithm. The system studied consists of an air compressor, a combustion chamber, a gas turbine, a dual pressure heat recovery steam generator and an absorption chiller in order to produce cooling, heating and power. The economic model used in their research was the TRR and the cost of the total system product was defined as our objective function and optimized using a genetic algorithm technique. Kavvadias and Maroulis [67] investigated the multi-objective optimization of a trigeneration plant. This optimization was carried out on technical, economical, energetic and environmental performance indicators in a multi-objective optimization framework. The results showed that trigeneration plants can be more economically attractive, energy efficient and environmental friendly than conventional cogeneration plants. Al-Sulaiman et al. [68] studied the thermoeconomic optimization of three trigeneration systems using organic Rankine cycles. The three systems considered were SOFC-trigeneration, biomass-trigeneration, and solar-trigeneration systems. The results showed that solar based trigeneration system has the highest net available exergy as compared to the other two systems. Therefore, it has the highest potential to have the highest exergy if the solar collector performance is improved. Wang et al. [69] conducted multi-objective optimization of an organic Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm. The multi-objective optimization of the ORC with R134a as the working fluid was conducted in order to achieve the system optimization design from both thermodynamic and economic aspects using non- dominated sorting genetic algorithm (NSGA-II). The decision variables considered for multi- objective optimization were turbine inlet pressure, turbine inlet temperature, pinch temperature 45PDF Image | Energy Systems for Multigeneration Purposes
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