Non-dominated sorting genetic algorithm (NSGA-II) was deployed in this paper for multi-objective thermo-economic optimization of biomass retrofit for an existing solar organic Rankine cycle (ORC) power plant. The existing plant consists of a field of linear Fresnel collectors (LFC), integrated directly with two-tank thermal energy storage (TES) system, which interfaces with ORC power block. The real solar-ORC plant currently runs at Ottana, Italy, albeit with some technical challenges basically due to inconsistent availability of solar irradiation. In order to upgrade the plant, a novel scheme had been proposed to install a biomass unit in parallel to the solar field, such that both LFC/TES and biomass furnace could directly and independently satisfy fractional thermal input requirement of the ORC. Being a retrofit system, existing design parameters of all the already operating units were imposed as equality constraints in this study, and the combustion excess air, as well as pinch point temperature difference of furnace heat exchangers that optimize the hybrid plant were investigated. Results showed that biomass mass flow rate of 0.133 kg/s and investment cost rate of 57 €/h are optimal for the studied biomass retrofit scheme. At this optimum point, excess air was obtained as 56%, furnace heater pinch point temperature difference as 28.8 °C and air pre-heater pinch point temperature difference as 38.5 °C. More generally, results showed that excess air value of less than 100%, furnace heater pinch point temperature difference of less than 80 °C, and air pre-heater pinch point temperature difference of less than 80 °C would optimize the studied biomass retrofit scheme.
|Titolo:||Multi-objective thermo-economic optimization of biomass retrofit for an existing solar organic Rankine cycle power plant based on NSGA-II|
|Data di pubblicazione:||2020|
|Tipologia:||1.1 Articolo in rivista|
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|Multi objective thermo economic optimization of biomass retrofit for an existing solar organic Rankine cycle power plant based on NSGA_II_2020.pdf||articolo online||versione editoriale||Open Access Visualizza/Apri|