Energy,exergy, and exergoeconomics (3E) analysis and multi-objective optimization of a multi-generation energy system for day and night time power generation - Case study: Dezful city |
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| Affiliation: | 1. Faculty of Mechanical Engineering, Mohaghegh Ardabili University, Ardabil, Iran;2. Faculty of Mechanical Engineering, Urmia University of Technology, Urmia, Iran;1. School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, China;2. College of Engineering / School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran;3. School of Environment, College of Engineering, University of Tehran, Tehran, Iran;1. Department of Mechanical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran;2. School of Mechanical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran;3. Department of Engineering, Università degli Studi di Perugia, Perugia, Italy;1. School of Environment, College of Engineering, University of Tehran, Tehran, Iran;2. School of Environment, College of Engineering, University of Tehran, Tehran, Iran |
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| Abstract: | The present study aimed to investigate a multi-generation energy system for the production of hydrogen, freshwater, electricity, cooling, heating, and hot water. Steam Rankine cycle (SRC), organic Rankine cycle (ORC), absorption chiller, Parabolic trough collectors (PTCs), geothermal well, proton exchange membrane (PEM) electrolyzer, and reverse osmosis (RO) desalination are the main subsystems of the cycle. The amount of exergy destruction is calculated for each component after modeling and thermodynamic analysis. The PTCs, absorption chiller, and PEM electrolyzer had the highest exergy destruction, respectively. According to meteorological data, the system was annually and hourly tested for Dezful City. For instance, it had a production capacity of 13.25 kg/day of hydrogen and 147.42 m3/day of freshwater on 17th September. Five design parameters are considered for multi-objective optimization after investigating objective functions, including cost rate and exergy efficiency. Using a Group method of data handling (GMDH), a mathematical relation is obtained between the input and output of the system. Next, a multi-objective optimization algorithm, a non-dominated sorting genetic algorithm (NSGA-II), was used to optimize the relations. A Pareto frontier with a set of optimal points is obtained after the optimization. In the Pareto frontier, the best point is selected by the decision criterion of TOPSIS. At the TOPSIS point, the exergy efficiency is 31.66%, and the total unit cost rate is 21.9 $/GJ. |
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| Keywords: | Artificial neural network Organic rankine cycle Reverse osmosis Solar radiation Exergoeconomics Multi-generation system |
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