Techno-economic optimization of a biomass gasification energy system with Supercritical CO2 cycle for hydrogen fuel and electricity production

被引:25
|
作者
Soltani, Mohammad Mohsen [1 ]
Ahmadi, Pouria [1 ]
Ashjaee, Mehdi [1 ]
机构
[1] Univ Tehran, Coll Engn, Sch Mech Engn, POB 11155-4563, Tehran, Iran
关键词
Biomass; Gasification; Hydrogen fuel; Thermodynamic analysis; Thermo -economic analysis; Artificial neural network; Process optimization; MULTIOBJECTIVE OPTIMIZATION; GAS-TURBINE; POWER; CELL; COGENERATION; PERFORMANCE; EXERGY; DESIGN;
D O I
10.1016/j.fuel.2022.126264
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Biomass is considered a carbon-neutral fuel and can play a significant role in obtaining sustainable energy generation. This study aims to raise the exergy efficiency and reduce costs in a novel energy system that is employed to generate power, heating, cooling, fresh water, and hydrogen fuel. Based on this, municipal solid waste (MSW), wood, and paper are considered as biomass fuels of the examined system and the system per-formance is evaluated from a thermodynamic and thermo-economic point of view through a parametric study. In the considered system, the generated gas in the gasifier supplies the energy required to run an externally fired gas turbine module. The waste heat of the gas turbine cycle is used to run a supercritical CO2 (S-CO2) power gen-eration cycle coupled with a heat exchanger to supply hot water and run an organic Rankine cycle (ORC) power generation cycle, as well as an absorption chiller cycle equipped with an ejector to produce cooling. In addition, the waste heat of the ORC is employed to produce fresh water through a humidification-dehumidification (HDH) desalination unit, a part of which is used to supply a proton exchange membrane electrolyzer (PEME) and the remaining is for other purposes. By considering the exergy efficiency and the levelized cost of energy (LCOE) as objective functions, a multi-objective optimization based on the genetic algorithm has been applied. An artificial neural network (ANN) plays an intermediating role in the optimization process to reduce the calculation time and raise the optimization speed. The relationship between the objective function and decision variables was analyzed using the ANN to determine the optimal point in this energy system. The results show that under optimal operating conditions, the exergy efficiency of the system with the biomass fuels of MSW, wood, and paper is equal to 41.21 %, 40.25 %, and 39.33 %, respectively. The LCOE values under the same conditions are 30.77 $/MWh, 32.06 $/MWh, and 33.6 $/MWh, respectively. Furthermore, at the optimum point, the amount of fresh water and hydrogen production in the system with the biomass fuels of MSW, wood, and paper is equal to 1.128 kg/s, 1.058 kg/s, 0.991 kg/s, and 9.355 g/s, 9.038 g/s and 8.438 g/s, respectively. Overall, the proposed system with the biomass fuel of MSW has shown good performance in terms of thermodynamics and cost-effectiveness.
引用
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页数:22
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