A new solar, natural gas, and biomass-driven polygeneration cycle to produce electrical power and hydrogen fuel; thermoeconomic and prediction approaches

Abstract

Nowadays, due to the limitations of fossil fuels utilization, renewable energies-driven polygeneration systems (PGSs) are becoming popular. In this article, the simulation and conceptual design of a PGS based on biomass and natural gas fuels to produce products such as electricity, hydrogen fuel, hot water (HW) and chilled water (CHW) is presented and discussed. The introduced PGS is integrated with units and cycles such as water electrolysis (based on polymer electrolyte membrane (PEM) electrolyzer), a ground source heat pump (based on geothermal well), and an absorption chiller. Additionally, in order to supply a fraction of the heat required for the cycle, a solar thermal unit (based on parabolic trough solar collectors, PTSCs) has been embedded. The proposed PGS is evaluated and analyzed from thermodynamic and exergoeconomic points of view. Moreover, as an innovative approach, a prediction on machine learning (ML) model is developed to predict and evaluate hydrogen production rate through biomass gasification. Besides, a state-of-art structure and configuration is considered for the proposed PGS, which had not been reported in the literature. The outcomes indicated that the considered PGS is capable of generating nearly 3.71 MW of electricity, 11.42 kg/h of hydrogen fuel, 5.7 kg/h of oxygen gas, 5.9 MW of CHW, and 2.95 MW of HW. In such a context, the energy and exergy efficiencies of PGS are yielded 74.2 % and 32.3 %, respectively. It is estimated that the implementation of such a system will require an investment cost of almost $ 3.9 million. It was also found that the HW unit exergy cost is approximately 37.8 % higher than that of CHW. Besides, the unit exergy cost of hydrogen fuel is almost 4.4-fold higher than that of electricity. In addition, a detailed parametric research is conducted to analyses the influence of the costing metrics on the PGS behavior under consideration. Finally, the results demonstrated that ML model had acceptable accuracy and could predict the hydrogen production rate through biomass gasification.