Go Bong Choi

Dynamic optimization of maintenance and improvement planning for water main system: Periodic replacement approach

This paper proposes a Markov decision process (MDP) based approach to derive an optimal schedule of maintenance, rehabilitation and replacement of the water main system. The scheduling problem utilizes auxiliary information of a pipe such as the current state, cost, and deterioration model. The objective function and detailed algorithm of dynamic programming are modified to solve the periodic replacement problem. The optimal policy evaluated by the proposed algorithm is compared to several existing policies via Monte Carlo simulations. The proposed decision framework provides a systematic way to obtain an optimal policy.

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.

A prioritization method for replacement of water mains using rank aggregation

Pipe breaks in municipal water distribution networks may cause serious damage economically and socially. Existing methods for replacement scheduling of pipes do not provide practical indicators for replacing an individual deteriorated pipe. This work formulates the selection problem as the decision of preference ordering or ranking and proposes a bipartite ranking-based approach. The suggested approach also considers loss from broken pipes in terms of the costs associated with broken water main and its repair. We use rank aggregation method to integrate multiple ranks into replacement order of water mains. The suggested framework prioritizes current pipe sections for replacement based on the aggregated ranks. Multiple ranks given by the reliability of water pipe sections are aggregated and a cost effective policy for pipe replacement is derived.

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

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.