Thermo-economic multi-objective optimization of an innovative Rankine–organic Rankine dual-loop system integrated with a gas engine for higher energy/exergy efficiency and lower payback period

2021 ◽  
Author(s):  
Sepehr Sanaye ◽  
Ali Ghaffari
Keyword(s):  
Payback Period ◽  
Exergy Efficiency ◽  
Loop System ◽  
Multi Objective Optimization ◽  
Gas Engine ◽  
Multi Objective

Thermodynamic and Economic Analysis and Multi-Objective Optimization of Supercritical CO2 Brayton Cycles

2015 ◽  
Author(s):  
Hang Zhao ◽  
Qinghua Deng ◽  
Wenting Huang ◽  
Zhenping Feng
Keyword(s):  
Supercritical Co2 ◽  
Exergy Efficiency ◽  
Temperature Cycle ◽  
Inlet Temperature ◽  
Brayton Cycle ◽  
Multi Objective Optimization ◽  
Turbine Inlet Temperature ◽  
Multi Objective ◽  
Turbine Inlet ◽  
Thermodynamic Performance

Supercritical CO2 Brayton cycles (SCO2BC) offer the potential of better economy and higher practicability due to their high power conversion efficiency, moderate turbine inlet temperature, compact size as compared with some traditional working fluids cycles. In this paper, the SCO2BC including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparative analyses on RBC and RRBC are conducted. Based on the thermodynamic and economic models and the given conditions, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power ($/kW) as its objectives. In addition, the Artificial Neural Network (ANN) is chosen to establish the relationship between the input, output, and the key cycle parameters, which could accelerate the parameters query process. It is observed in the thermodynamic analysis process that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch temperature difference of heat exchangers have significant effects on the cycle exergy efficiency. And the exergy destruction of heat exchanger is the main reason why the exergy efficiency of RRBC is higher than that of RBC under the same cycle conditions. Compared with the two kinds of SCO2BC, RBC has a cost advantage from economic perspective, while RRBC has a much better thermodynamic performance, and could rectify the temperature pinching problem that exists in RBC. Therefore, RRBC is recommended in this paper. Furthermore, the Pareto front curve between the cycle cost/ cycle power (CWR) and the cycle exergy efficiency is obtained by multi-objective optimization, which indicates that there is a conflicting relation between them. The optimization results could provide an optimum trade-off curve enabling cycle designers to choose their desired combination between the efficiency and cost. Moreover, the optimum thermodynamic parameters of RRBC can be predicted with good accuracy using ANN, which could help the users to find the SCO2BC parameters fast and accurately.

Thermodynamic and Economic Analysis and Multi-objective Optimization of Supercritical CO2 Brayton Cycles

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Hang Zhao ◽  
Qinghua Deng ◽  
Wenting Huang ◽  
Dian Wang ◽  
Zhenping Feng
Keyword(s):  
Supercritical Co2 ◽  
Pressure Ratio ◽  
Economic Modeling ◽  
Exergy Efficiency ◽  
Temperature Cycle ◽  
Inlet Temperature ◽  
Brayton Cycle ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thermodynamic Performance

Supercritical CO2 Brayton cycles (SCO2BC) including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparative analyses on RBC and RRBC are conducted. Nondominated Sorting Genetic Algorithm II (NSGA-II) is selected for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power (k$/kW) as its objectives. Artificial neural network (ANN) is chosen to accelerate the parameters query process. It is shown that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch temperature difference of heat exchangers have significant effects on the cycle exergy efficiency. The exergy destruction of heat exchanger is the main reason why the exergy efficiency of RRBC is higher than that of the RBC under the same cycle conditions. RBC has a cost advantage from economic perspective, while RRBC has a much better thermodynamic performance, and could rectify the temperature pinching problem that exists in RBC. It is also shown that there is a conflicting relationship between the cycle cost/cycle power (CWR) and the cycle exergy efficiency. The optimization results could provide an optimum tradeoff curve enabling cycle designers to choose their desired combination between the efficiency and cost. ANN could help the users to find the SCO2BC parameters fast and accurately.

scholarly journals Multi-Objective Optimization of Organic Rankine Cycle for Low-Grade Waste Heat Recovery

2019 ◽  
Vol 118 ◽  
pp. 03053
Author(s):  
Ruijie Wang ◽  
Jingquan Zhao ◽  
Lei Zhu ◽  
Guohua Kuang
Keyword(s):  
Waste Heat ◽  
Organic Rankine Cycle ◽  
Rankine Cycle ◽  
Exergy Efficiency ◽  
Condensation Temperature ◽  
Low Grade ◽  
Multi Objective Optimization ◽  
Working Fluids ◽  
Multi Objective ◽  
Evaporation Temperature

The organic Rankine cycle (ORC) is considered as one of the most viable technology to recover low-grade waste heat. A multi-objective optimization model is established to simultaneously derive the maximum exergy efficiency and the minimum electricity production cost (EPC) of a specific ORC system by employing the genetic algorithm (GA). Evaporation temperature and condensation temperature are selected as decision variables. At first, variations of exergy efficiency and EPC with evaporation temperature and condensation temperature are investigated respectively using R245fa, R245ca, R600, R600a, R601 and R601a as working fluids. Subsequently, a multi-objective optimization is performed and the Pareto frontiers for various working fluids are obtained. Results indicate that performance of the specific ORC system with R245fa as working fluid is better that with other working fluids.

Multi-objective optimization of a geothermal steam turbine combined with reverse osmosis and multi-effect desalination for sustainable freshwater production

2021 ◽  
pp. 1-19
Author(s):  
Aida Farsi ◽  
Marc A. Rosen
Keyword(s):  
Steam Turbine ◽  
Reverse Osmosis ◽  
Exergy Efficiency ◽  
Mathematical Function ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Cost Rate ◽  
Multi Objective ◽  
Design Variables ◽  
Seawater Salinity

Abstract A novel geothermal desalination system is proposed and optimized in terms of maximizing the exergy efficiency and minimizing the total cost rate of the system. The system includes a geothermal steam turbine with a flash chamber, a reverse osmosis unit and a multi-effect distillation system. First, exergy and economic analyses of the system are performed using Engineering Equation Software. Then, an artificial neural network is used to develop a mathematical function linking input design variables and objective functions for this system. Finally, a multi-objective optimization is carried out using a genetic algorithm to determine the optimum solutions. The Utopian method is used to select the favorable solution from the optimal solutions in the Pareto frontier. Also, the distributions of the values of design variables within their allowable ranges are investigated. It is found that the optimum exergy efficiency and total cost rate of the geothermal desalination system are 29.6% and 3410 $/h, respectively. Increasing the seawater salinity and decreasing the intake geothermal water temperature results in an improvement in both exergy efficiency and total cost rate of the system, while variations in the flash pressure and turbine outlet pressure lead to a conflict between the exergy efficiency and total cost rate of the geothermal desalination system over the range of their variations.

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