Machine learning optimization and 4E analysis of a CCHP system integrated into a greenhouse system for carbon dioxide capturing

Abstract

The world has seen an increase in the popularity of renewable-based energy systems. However, because of their erratic nature, fossil fuels cannot be entirely phased out. Utilizing carbon dioxide in greenhouses close to power plants and generating extra power electricity from dissipated thermal energy are appealing strategies that cause us to have a cleaner environment and affordable power electricity. To exploit the high enthalpy of expelled gases from a gas turbine, the gas turbine was coupled with an absorption refrigerant cycle and an organic Rankine cycle and analyzed. Additionally, a deep artificial neural network method was used to calculate all functions in a rapid optimization and accurate 4E analysis. The carbon dioxide emissions subsided from 0.9183 to 0.41 kg CO2/s, and the optimization time improved 257 times after using the trained machine learning model. An adsorption technique for CO2 separation is used for the proposed system because of its lower energy consumption. The maximum exergy and energy efficiencies were attained at 36.71% and 49.2%, respectively, and parametric studies are being assessed. Due to increases in harvests and efficiencies, the net annual interest has increased by 21.8%, up to 22.5 million dollars.