Techno-economic-environmental study of an innovative solar-boosted system to produce low-emission hydrogen and methanol: ANN-based optimization

Abstract

In the present study, techno-economic-environmental optimization of a solar-boosted energy system generating power, freshwater, and methanol as a promising clean fuel has been undertaken. A heliostat field with variable mirrors is used in a gas turbine cycle to decrease the necessity of burning much hydrocarbon fuel. By using pressure swing adsorption, 80% of the contained carbon dioxide of the exiting stream of the gas turbine is recovered and stored. In addition, by exploiting the waste heat of the exiting stream of the gas turbine, an organic Rankin cycle, a multi-effect distillation with thermal vapor compression, and a single effect absorption chiller are driven. Furthermore, a proton exchange membrane electrolyzer is utilized to generate hydrogen and portions of stored hydrogen and carbon dioxide go through the methanol synthesis reaction to produce desired methanol. A comprehensive parametric study through the 4E analysis has been carried out to detect the influential design parameters to set the decision variables for the optimization process. In the end, due to the complexity of the system, a deep neural network is developed to lower the computational time. The findings of the deep neural network-based optimization show the optimal solution in which the total cost rate of the system, exergy efficiency, and the emitted carbon dioxide values are 1.26 $/s, 46.25%, and 0.58 kg/s, respectively.