As the transition from fossil fuel-based systems to sustainable energy systems progresses, the demand for new processes is increasing across most industries. I have been conducting research to improve the sustainability of new processes in terms of both economic and environmental performance. Novel processes are conceptually designed using process simulation tools (e.g. Aspen Plus, Aspen HYSYS) and subsequently evaluated for performance through techno-economic analysis (TEA) and life cycle assessment (LCA).During conceptual process design, I sought to improve process performance by employing Bayesian optimization to identify optimal operating conditions. Additionally, I developed mathematical models for the thermodynamic properties of materials and the performance of equipment to enhance the accuracy of simulation models. Furthermore, comprehensive analyses are conducted, including uncertainty and sensitivity analyses, to assess how process performance varies under market fluctuations and regulatory policies. This approach ensures a thorough evaluation of the robustness and feasibility of new processes. Specifically, my research has focused on developing novel carbon capture processes that could replace conventional amine-based absorption technologies, as well as designing processes for producing hydrogen in a cleaner and more sustainable manner.

• Designing cleaner hydrogen production processes aimed at achieving the economic targets set by the Hydrogen Shot initiative. 

• Novel carbon capture processes that are more cost-effective and energy-efficient compared to conventional amine-based carbon capture technologies. 

• Exploring the industrial application of carbon dioxide electrolysis at the process level and identifying strategies to achieve economic feasibility. 

• Improving process performance through the utilization of industrial waste energy and materials, such as LNG cold energy and wastewater. 

Anthropogenic carbon emissions from various industries are increasing worldwide, further worsening global warming. In particular, energy-related industries contribute a significant share of these emissions. Among energy-related emission sectors, power and industrial sectors account for more than 60% of total carbon dioxide emissions. To achieve carbon reduction in these sectors, a comprehensive understanding of the accurate quantification and interpretation of carbon emissions is required. Currently, life cycle assessment (LCA) is an efficient method for quantifying and interpreting the environmental impacts of most industrial systems. LCA is a well-suited method for evaluating product systems that have a substantial impact throughout all stages of their life cycle, from production to the end-of-life phase. The LCA method can be utilized in various industries and is a valuable decision-making tool that enables companies to assess and improve the environmental performance of their products. However, LCA has some limitations in engineering applications, especially at the industrial-plant level. These include reliance on generalized datasets, subjective interpretation of multiple environmental metrics, and a lack of detailed guidance for process-level design and optimization. To overcome these limitations, I strive to develop a novel LCA-free carbon evaluation framework called Process-Level Carbon Contribution Analysis (PCCA). I am particularly interested in utilizing these methods to develop carbon analysis techniques and a carbon reduction framework. Ultimately, the goal is to standardize and universalize carbon-neutral engineering for industrial applications. 

• LCA analysis for new sustainable processes involves evaluating the environmental impacts associated with all stages of the process life cycle.

• Development of a novel environmental analysis method to overcome the limitations of LCA, and the creation of a new process-level carbon analysis framework through the integration of this method with LCA. 

• Economic evaluation and optimization considering carbon taxes and carbon regulations, aimed at balancing cost-efficiency.

The traditional crude oil-based refinery industry is structured in a top-down manner, supported by well-established infrastructure and a large, stable industrial scale. However, with growing concerns over sustainability and carbon emissions, there has been increasing interest in utilizing biomass and plastics as feedstocks and fuels in newly proposed refinery systems. While these systems are promising, they have yet to achieve the economic viability and technological maturity required for industrialization.In this context, diverse evaluations and innovative technologies are essential to facilitate a realistic transition. Reliable economic assessments and rigorous carbon emission analyses of emerging technologies are necessary to reduce uncertainties and establish a foundation for their proactive adoption. Beyond evaluating technologies at the process level, there is a need for studies leveraging Integrated Assessment Models (IAMs) to consider various environmental, economic, and climatic factors. Such research will provide a comprehensive perspective on the practical transformation of industries toward sustainability. Additionally, this research explores how these emerging technologies can be optimally integrated with renewable energy sources and how global networks can be established to maximize their efficiency and scalability. 

• Design and evaluation of new refinery processes utilizing plastics and biomass as feedstocks and fuels, focusing on their technical, economic, and environmental performance. 

• Establishment of a technological progress framework to tackle uncertainties in captial expenditure (CAPEX) estimation and greenhousgases (GHG) emissions assessment for emerging technologies.

• Evaluation and improvement of spatio-temporal realistic transitions using Integrated Assessment Models (IAMs), incorporating spatial and temporal dynamics to support effective and sustainable industrial transition. 

• Identifying transition points using new indicators, providing guidelines for energy policy formulation, and deriving optimal solutions for complex systems with renewable energy source. 

• Exploration of various applications of AI in the chemical industry, focusing on its potential to optimize processes, enhance sustainability, and drive innovation. 

• Development of a hybrid model combining first-principle approaches and machine learning to leverage the strengths of both methods for enhanced accuracy and efficiency in process modeling and optimization. 

• Enhancing control performance by integrating dynamic simulation with machine learning, enabling more accurate, adaptive, and efficient process operation.