Optimal Investment Strategy Analysis of On-Site Hydrogen Production Based on the Hydrogen Demand Prediction Using Machine Learning

Abstract

In order to achieve the hydrogen economy and respond to initial hydrogen demand appropriately, a hydrogen production and operation methodology is required to secure the economic feasibility of long-term on-site HRS. This study proposes a novel investment strategy for on-site hydrogen production to meet future hydrogen demand. The optimal investment strategy based on the dual-modal mode of combined autothermal reforming (ATR) and steam methane reforming (SMR) is proposed for hydrogen production using natural gas (NG) as a raw material. To predict hydrogen demand from 2020 to 2030, the machine learning (ML) technique was adopted. R2 and MSE as result using ML were 0.9936 and 6.88×10−5, respectively. In addition, the ATR-SMR hydrogen strategy (ASHS) process was analyzed and compared with the SMR-SMR and ATR-ATR hydrogen strategy (SSHS and AAHS) processes in terms of optimal operation rate, storage tank management, economics, and environmental impacts. The operation rate of proposed hydrogen production processes was determined by the hydrogen demand and storage tank level, and the optimal investment plan to install additional hydrogen process depends on the total amount of hydrogen production. In this study, these results were observed due to the effective combination of the strengths of ATR and SMR. Consequently, the ASHS had the best cost-effectiveness (LCOH at $5.63/kg H2) and environmental friendliness (unit CO2eq emissions at 10.21 kg CO2eq/kg H2 and 1.73 kg CO2eq/kg H2 with CCS). This study includes sensitivity analysis and a comparison of CO2 taxes by the country for three proposed hydrogen production processes. It could contribute to the optimal operation of the on-site hydrogen production system in preparation for future hydrogen demand.