Polyethylene waste gasification syngas analysis and multi-objective optimization using central composite design for simultaneous minimization of required heat and maximization of exergy efficiency

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.

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

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4032666 ◽

2016 ◽

Vol 138 (8) ◽

Cited By ~ 12

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.

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Multi-objective optimization of power, CO2 emission and exergy efficiency of a novel solar-assisted CCHP system using RSM and TOPSIS coupled method

Renewable Energy ◽

10.1016/j.renene.2021.12.078 ◽

2021 ◽

Author(s):

Navid Mahdavi ◽

Parisa Mojaver ◽

Shahram Khalilarya

Keyword(s):

Co2 Emission ◽

Exergy Efficiency ◽

Multi Objective Optimization ◽

Coupled Method ◽

Multi Objective

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

E3S Web of Conferences ◽

10.1051/e3sconf/201911803053 ◽

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.

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