Research

Topic 1 : Process synthesis and development

Current project: Upcycling of landfill gas into methanol

• Recently, many studies on alternative uses of biogases, particularly, LFG, have been performed with an emphasis on the value of LFG as an energy source from technical, economic, and environmental perspectives.

• To propose a high energy-efficiency, competitive economics and environmental-friendly MeOH production process from LFG, a plant-wide optimization was used in process synthesis.

• The optimal operation conditions (e.g. temperature and pressure) and major design specifications (e.g. reactor length and number of tubes) are determined using an optimization algorithm in Aspen Plus.

Topic 2 : Life cycle and techno-economic analysis

Current project: Upcycling of CO2 into hydrocarbons

• Olefins, which are primary produced from petroleum and natural gas, are important chemical building blocks. Due to the rapid depletion of petroleum sources, many alternative feedstock and technological pathways have been investigated.

• Here, the novel process of light hydrocarbons production from captured CO2 and renewable H2 was developed and evaluated using Aspen Plus Simulator.

• Especially, techno-economic analysis(TEA) and Life cycle assessment(LCA) were used to evaluate the techno-economic feasibilities and environmental impact of the proposed process, identify bottleneks for process improvement such as production cost and reducing emission.

Topic 3 : Optimization-based planning and scheduling

Motivating project: Scheduling of pharmaceutical intermediates manufacturing plant

• The pharmaceutical industry takes tremendous manufacturing time due to the decision on the integration of target material identification and unit operation combination.

• To improve the manufacturing process, the production schedule such as enterprise-scale resource management has been a challenging task.

• Recently, the rapid and accurate material management capability of the electronic track tage devices ahs been prvoed thereby expecting the production schedule improvement. Radio-fequency identification(RFID) is a salient example of the tracking device for optimizing device for optimizing process operation, inputting materials, and enabling efficient delivery.

• RFID-based integrated decision making frameworks is developed to provide efficient logistic plans for the pharmacutical intermediates manufacturing plant. Also, the production planning strategy was optimized by using mixed-integer linear programming(MILP).

Topic 4 : Supply chain optimization and smart logistics

Current project: Energy network design and optimization

• Supply chain optimization indicates the decision making for the design of a system using an optimization model with objective functions (e.g., minimum production costs, maximum profit).

• Developing an optimization model which accounts for the various constraints of the system as well as the location of supply and demand

• Applying multi-scale modeling technique to improve the operational flexibility of the system including process operation, facility planning, location problem, and scheduling.

• Recently, Geographic Information Systems (GIS) offer as a platform many advantages and can play a significant role in supply chain optimization and smart logistics.

Topic 5 : Renewable and carbon-neutral systems

Considering various RESs, new and existing harvesting and conversion technologies, as well as energy portfolio including electricity, heat hydrocarbon fuels, and chemicals (e.g.,hydrogen).

Current project: Renewable energy systems for an off-grid electrified city

• A global cost map to provide a comprehensive understanding and practices for the preliminary assessment and implementation of renewable energy source (RES) based energy systems.

• Using an optimization technique to minimize the system cost, we first assessed the technical and economic feasibility of six RES energy systems of three RES types (solar-powered, wind-powered, and solar/wind hybrid) in two cities (Barcelona, Spain, and Jeju Island, Korea).

Current project: Sustainable hydrogen infrastructure

• Hydrogen economy could be one of the promising systems to resolve problems of current fossil fuel-based energy such as resource depletion and climate change.

• On the LEAP platform, we developed a new hydrogen pathway-embedded energy-economic model of Korea.

• We then identified optimal hydrogen infrastructure using a optimization model formulated as mixed integer linear programming (MILP).

Current project: Upcycling of CO2 into hydrocarbons

• S2P framework, a new energy technology to produce liquid hydrocarbon fuels by reenergizing CO2 using sunlight, offers a number of potential benefits in the areas of energy security, sustainability, and climate change.

Current project: biomass for value-added chemical production

• A new framework for the synthesis and evaluation of novel biomass-to-fuel conversion strategies using Biomass Utilization Superstructure (BUS).

• The framework is capable of identifying, integrating, and evaluating B2F strategies using a formulated optimization models.

• We are working to extend the framework capability to aim at ;

  • Improving product functionality by compound-scale modeling.

  • Supporting all decision-makings for establishing a new biorefinery (e.g., determining facility, location, logistic, feedstock, and products)

Current project: Carbon capture & utilization (CCU4E) framework

• The CO2 capture and utilization of chemicals and fuels can indirectly mitigate CO2 emission by replacing the use of fossil fuels and enable the chemical industry to less depends on fossil resources.

• We have developed CCU4E and examined the techno-economic and environmental performance of the CO2-to-fuel pathways in the different uses of conventional and renewable resources.

• There is always a trade-off between the economic benefits and environmental impacts when using renewable H2/utility and conventional resources.

Topic 6 : Fault diagnosis and risk management

• Risk assessment is an essential step for preventing severe accidents and improving safety by managing identified risks.

• It is important to analyze all possible deviations in process equipment during process operation that can lead to any potential risks.

• To develop a set of risk acceptance criteria and a suitable risk assessment methodology, we performed four main tasks: risk identification, consequence analysis, frequency analysis, and risk evaluation.

Of Data-driven research, the challenge pertains to:

• Constructing a material database

• Developing an accurate property prediction model

• Offering a material design strategy

Our research is focused on:

• Predicting and discovering a material having the desired property

• Optimizing material synthesis and operation scheme

• Facilitating the interaction between lab-scale research and scale-up deployment

Our long-term research goal:

• Developing more rapid and accurate material R&D strategies

Topic 1 : AI-guided material discovery and optimization

• Artificial intelligence (AI) provides a  practical set of algorithms that could help to resolve complex systems in natural phenomena. Because AI is able to learn the hidden pattern and predict the behavior of such systems.

• Traditionally, material R&Ds need a theoretical approach for real-world application. However, identifying such principles is often time-consuming and expensive.

• To promote and envisage the rapid responsiveness of the material application, we develop an AI-driven approach.

• For AI methods, artificial neural networks (ANNs), support vector machine (SVM), and random forest (RF) are often utilized. Then, we use accuracy metrics to verify the prediction capability of an AI-driven approach.

Topic 2 : Deep learning and predictive analytics for smart factory

Current project: Decision-making for smart planning and operation of a petrochemical plant

• The petrochemical companies need to consider a risk management system to ensure sustained profit margins against the uncertainty of raw material prices, whereas it is difficult for most petrochemical companies to establish a precise production plan owing to the changeable prices of raw materials and final products.

• To handle the financial risk caused by the variation of raw materials and petrochemical prices and to establish sustainable operation strategies, a number of petrochemical companies have adopted a hedge trade system.

• The data-driven optimization model determines the decision variables for the adaptive operation of a petrochemical plant, such as quantinty, timing and prices of raw materials and products, and inventory management strategy operability as well as unit operation policies.

Topic 3 : Multi-scale modeling and optimization for renewable Energy network

• Integrating the technologies along with resources and demands to determine the optimal supply strategy.

• Designing the energy supply chain from resource extractions to energy transportation and distribution for strategic planning sustainable infrastructure.

• Developing a roubst dynamic operating model which accounts for RES variability as well as uncertainty on demand and prices.

• Applying multiscale modeling technique to improve the operational flexibility of the RES system including process operation, facility planning and scheduling, and business strategy.

Digital twin for complex systems predictive analytics

Computational quantum modeling and analytics