Joohyun Shin

TL;DR: Connections with another ML branch of reinforcement learning are elucidated and its role in control and decision problems is discussed, and implications of these advances for the fields of process and energy systems engineering are discussed.

TL;DR: This paper provides an introduction to Reinforcement Learning technology, summarizes recent developments in this area, and discusses their potential implications for the field of process control, and more generally, of operational decision-making.

TL;DR: In this article, a two-stage stochastic programming (2SSP) for day-ahead UC and dispatch decisions is combined with a Markov decision process (MDP) evolving at a daily timescale.

TL;DR: A multi-timescale decision-making model that combines Markov decision process (MDP) and mathematical programming (MP) in a complementary way is developed and a computationally tractable solution algorithm based on reinforcement learning (RL) is introduced to solve the MP-embedded MDP problem.

TL;DR: In this article, pilot tests for lube hydroprocessing, which is composed of hydrotreating/cracking followed by hydroisomerization, are implemented with three different types of feedstocks (paraffinic, intermediate, and naphthenic vacuum gas oils) and under different reaction conditions (catalyst and temperature).