Triple-objective optimization using ANN+NSGA-II for an innovative biomass gasification-heat recovery process, producing electricity, coolant, and liquefied hydrogen

Abstract

A novel thermal integration approach is introduced for a biomass-driven gas turbine power plant that generates electricity, coolant, and liquefied hydrogen. The designed scheme encompassed an organic flash cycle, a bi-evaporator ejector refrigeration unit, a high-temperature water electrolyzer for hydrogen production, a multi-effect desalination cycle for supplying water for electrolysis process, and a Claude cycle for hydrogen gas liquefaction. The designed system's importance comes back to using biomass feedstock as the input fuel and utilizing the hydrogen liquefaction method. In addition to the possibility of deploying the system in remote areas, it provides the opportunity for hydrogen storage in a smaller volume for more accessible storage and transportation. On the other hand, using a comparative method for selecting an environmentally friendly fluid for the heat recovery subsystem is another crucial aspect of the present study from the environmental aspect. It is found that R161 is the appropriate choice among the seven studied working fluids. Subsequently, a comprehensive evaluation of the entire system's thermodynamic and environmental aspects is performed using an intelligent process. By considering energy and exergy efficiencies along with CO2 emissions as the objective functions, a thorough sensitivity analysis and a triple-objective optimization are carried out. Hence, artificial neural networks for the objectives are developed and integrated into the NSGA-II optimization method. Employing LINMAP decision-making, the values of objective are attained, exhibiting 39.6 % for energy efficiency, 36.1 % for exergy efficiency, and 631.7 kg/MWh for CO2 emissions. Considering the optimum solution, the proposed system is capable of producing electricity, cooling, and liquefied hydrogen with capacities of 4526 kW, 1875 kW, and 21.22 m3/day, respectively. Additionally, the scenario yields an exergoenvironmental index of 0.579 and an exergetic stability index of 0.61. and a liquefied hydrogen generation rate of 21.22 m3/day.