Framework for Feasibility Study of Low-Carbon Ammonia Utilization Strategies in the Energy Sector: Stochastic Optimization, Techno-Economic Analysis, and Machine Learning Predictions

Abstract

This in-depth research investigates the strategic deployment of ammonia for hydrogen production and electricity generation in the Republic of Korea. Ammonia, chosen for its versatile attributes, offers efficient and high-density energy storage capabilities, addressing challenges associated with renewable energy integration. Its adaptability, ease of transport, and established global supply chains make it a compelling candidate for transitioning towards a cleaner and more resilient energy landscape. This study, comprising historical trading data analysis, stochastic optimization, process simulation, economic evaluation, carbon footprint analysis, and machine learning-based regression, emerges as a deliberate exploration of the untapped potential of ammonia within the energy sector. Commencing with an examination of historical ammonia trading data, the research unveils the dynamics of transactions between the Republic of Korea and other nations, providing a foundation for subsequent optimization and simulation phases. Stochastic optimization takes a central role in addressing the inherent uncertainty in the dynamic energy landscape. The research develops and applies stochastic optimization models to optimize ammonia use for hydrogen production and electricity generation, providing robust and adaptable strategies for maximizing efficiency and minimizing costs. Process simulation becomes pivotal, offering a detailed understanding of the technical aspects involved in ammonia-based hydrogen production and electricity generation. This phase identifies optimal operational parameters and configurations. Economic and carbon footprint analyses are integral, evaluating the financial viability and environmental impact of the proposed applications. The economic analysis scrutinizes cost-effectiveness, considering factors such as capital investment, operational expenses, and revenue generation. Concurrently, the carbon footprint analysis quantifies environmental implications, providing insights into the sustainability of the proposed energy solution. Machine learning-based regression enhances optimization results. Leveraging advanced regression algorithms and the PyCaret library, the study systematically compares and selects models that best predict optimization outcomes. This data-driven approach refines predictive accuracy and uncovers hidden relationships within the complex system. In conclusion, this study presents a holistic approach to the application of ammonia for hydrogen production and electricity generation in the Republic of Korea. From historical trade analysis to stochastic optimization, process simulation, economic evaluation, and machine learning-based regression, the study contributes to a nuanced understanding of challenges and opportunities in integrating ammonia-based solutions into the energy landscape. The findings offer valuable insights for policymakers, industry stakeholders, and researchers navigating the evolving landscape of sustainable energy solutions in the Republic of Korea.