Deep learning optimization of a biomass and biofuel-driven energy system with energy storage option for electricity, cooling, and desalinated water

To improve the turnover of thermodynamic cycles, combined cycles have gained a great deal of interest today. The primary objective of these systems is to maximize the utilization of wasted energy from power cycles to initiate cooling, heating, and desalination cycles. In the context of this project, the general cycle comprises a primary portion of power generation, the generation of freshwater, and cooling along with the essential heating of water. Additionally, compressed air energy storage was utilized to lower the expense of the complete cycle. Because of this, we should switch to using compressed air during the off-peak hours of the day and night when the power demand is at its highest. This article also includes a simulation of the gasification process, in which the higher temperature of the generated products is utilized to pre-heat the air. Considering each set of decision variables, the duration of each simulation ranges from 10 to 15 s. It is vital to utilize machine learning techniques to decrease the time needed for optimization to discover the ideal points. In conclusion, the genetic algorithm demonstrated that the exergy turnover and economic cost of the optimal point of the newly introduced cycle are equivalent to 36.21% and 6.56 $/h, respectively.

Automated summary

To enhance the efficiency of thermodynamic cycles, combined cycles have become a subject of great interest. These systems aim to maximize the utilization of waste heat from power cycles to drive cooling, heating, and desalination cycles. In this project, the general cycle includes power generation, freshwater generation, and cooling with essential water heating, while compressed air energy storage is employed to reduce the overall cost. Therefore, it is suggested to use compressed air during peak hours when power demand is highest. The article also presents a simulation of the gasification process using the generated products' high temperature to pre-heat the air. To expedite the optimization process and find the ideal points, machine learning techniques should be employed. The genetic algorithm demonstrates that the exergy turnover and economic cost of the newly introduced cycle's optimal point are 36.21% and 6.56 $/h, respectively.