오태훈 교수

Abstract As the digital transformation of the bioprocess is progressing, several studies propose to apply data‐based methods to obtain a substrate feeding strategy that minimizes the operating cost of a semi‐batch bioreactor. However, the negligent application of model‐free reinforcement learning (RL) has a high chance to fail on improving the existing control policy because the available amount of data is limited. In this article, we propose an integrated algorithm of double‐deep Q‐network and

To mitigate the online computational load of model predictive control, move blocking, which parameterises either the input sequence or offset from the base sequence by fixing the decision variables over arbitrary time intervals, is commonly used. However, existing move blocking schemes use a fixed base sequence only and do not fully exploit the valuable properties from various base sequences. Thus, we propose the interpolated solution-based move blocking strategy which parameterises the offset f

To obtain aromatic compounds from a crude mixture such as reformate or pyrolysis gasoline, three different processes are simulated with the realistic composition of reformate and product specification. Simulations were performed by Aspen Plus supported with COSMO-RS method to predict the physical and thermodynamic properties of ionic liquid. Furthermore, utility analysis and economic evaluation are presented. Conventionally, aromatic compounds are extracted from a crude mixture either by extract