Seok Goo Lee

Modeling and Simulation of Ship Transport of CO2

Abstract With growing concern about greenhouse gas emission, previous carbon capture and storage (CCS) research has mainly focused on efficient capture methods. However, there is a relative lack of studies on ship transport and offshore unloading. The available guidelines, if any, are simply suggested without a systematic analysis from the viewpoint of the optimal transport chain system. Thus, this study addresses the issue by modeling the ship-based transport network of CO2. In particular, liquefaction, boil off gas (BOG) reliquefaction and offshore unloading processes are investigated to provide essential guidelines in terms of an optimal thermodynamic state (pressure-temperature: P-T). Optimal compression ratio and pressure-enthalpy flash are implemented on each compression-intercooling-separation stage, and a conceptual model for BOG reliquefaction process is also proposed.

Dynamic matrix control applied on propane-mixed refrigerant liquefaction process

This study proposes a dynamic matrix control strategy that produces control input sequences which are more robust and reduce power consumption than conventional proportional-integral (PI) controllers when applied to the C3MR liquefaction process. First, a rigorous process dynamic model was constructed in Aspen HYSYS Dynamics 7.3 and MATLAB 2014a which calculates dynamic responses for two different scenarios of unmeasured step disturbances increasing the load of liquefaction energy. Then, a DMC module including the manipulation of the compressor speed was formulated. The simulations using the proposed DMC module demonstrate that the multivariable optimal control increases the energy efficiency and robustness of a complex liquefaction cycle process.

Modeling and stochastic dynamic optimization for optimal energy resource allocation

Abstract With ever-growing global demand for energy and severe environmental regulations, optimal management of energy distribution system and policy is becoming an important problem for many countries. We present a stochastic dynamic model that describes energy resource allocation under uncertainty and derive an optimal policy for long-term investments in novel energy technologies. Probabilistic model based on Markov chain that balances the demands and supplies are developed considering the city boundaries and electric power system in South Korea. We propose an algorithmic strategy based on the framework of approximate dynamic programming and demonstrate its efficacy using a prototypic example with the available data

Simulation and Optimization of an Integrated CO2 Capture and Storage System

Abstract Carbon dioxide capture and storage (CCS) system has been developed during several decades. However, most of studies about CCS system focus on a specific process, while a few does on the integrated CCS system. Further study about process design and optimization for the integrated CCS system is needed to deal with process consistency and decision making about choosing optimal design option of overall system. This study presents a bottom-up approach for designing an integrated CCS system. In first step, optimal design of each CCS unit process is proposed using previous researches and power plant operation data. Then all of designed process models are unified to construct a model of an integrated CCS system. Based on this model, proper integration and optimization study for designing whole CCS system is shown. Additionally, this study describes a list of design variables that have much influence on designing the integrated CCS process.

Optimal planning of energy management system under demand uncertainty

Abstract Energy management problem is a significant challenge as energy demand gets higher and securing resource is not always guaranteed. This study addresses the problem of modelling energy resource allocation and deriving on optimal policy for long-term investments. Model is constructed in Markov chain, which enables us to construct a probabilistic model that balances demand and supply using the data reflecting the current situation of South Korea. A large number of states with uncertainty make the resulting stochastic optimization problem near impossible to solve. We also propose an algorithmic strategy based on the framework of approximate dynamic programming and show this method is effective in solving the decision making problem.