Gas turbine-based system taking advantage of LNG regasification process for multigeneration purposes; Techno-economic-environmental analysis and machine learning optimization.

It is viable for engineers to determine how thermodynamic systems can benefit from heat recovery in order to improve efficiency, reduce costs, and lower carbon dioxide emissions. The authors of the study propose a new energy system that incorporates various cycles and units, including gas turbines, Rankine and two organic Rankine cycles, an absorption refrigeration unit, a proton exchange membrane (PEM) electrolyzer, and a liquefied natural gas (LNG) unit. The hydrogen produced is stored and transferred for use as fuel. The system was modeled and optimized using MATLAB programming software, with a parametric study and sensitivity analysis conducted before employing a genetic algorithm optimization to find the most suitable performance conditions. Modifying the compressor's pressure ratio within the range of 6–18 caused a shift in the cooling load, ranging from 45 to 36 MW. Nonetheless, these adjustments in pressure ratio yielded a reduction of 0.4 Cent/kWh in levelized cost of electricity (LCOE) and 0.15 $/kg in levelized cost of hydrogen (LCOH). In the base design mode, the total cost rate is 5715.3 $/h, the exergy efficiency is 39.03%, and the normalized CO 2 emissions are 335.89 kg/GJ. However, the optimization results showed a reduced total cost rate of 1884.50 $/h, along with an improved exergy efficiency from 39.03% to 40.32% and a decrease in annual CO 2 emissions from 211,000 metric tons to 175,000 metric tons. The implementation of artificial neural networks (ANN) has significantly decreased the time required for optimization from 47 h to just 16 min. [ABSTRACT FROM AUTHOR]