Deep Learning and AutoML for Dynamic Modeling of LNG Regasification Process Using Seawater

Shin, Yong‐Beom; Shin, Dongil

doi:10.1016/b978-0-323-88506-5.50250-3

Cited by 3 publications

(1 citation statement)

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“…In addition to this, there have been recent endeavors to predict vaporizer performance through machine learning, leveraging extensive field data. Notably, Shin et al (2021) introduced a dynamic prediction model for determining NG outlet temperature and discharge seawater temperature in response to variations in an ORV's seawater flow rate, seawater temperature, and LNG flow rate [17]. The ensuing results indicated that the predictive accuracy as measured in MES followed the sequence of LSTM, AutoML, and FNN.…”

Section: Introductionmentioning

confidence: 99%

Development of a DNN Predictive Model for the Optimal Operation of an Ambient Air Vaporizer of LNG

Shin,

Lim,

Kim

et al. 2023

Processes

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In this study, we conducted preliminary research with the objective of leveraging artificial intelligence to optimize the efficiency and safety of the entire Ambient Air Vaporizer (AAV) system for LNG (Liquid Natural Gas). By analyzing a year-long dataset of real operational data, we identified key variables that significantly influence the outlet temperature of Natural Gas (NG). Based on these insights, a Deep Neural Network (DNN) prediction model was developed to forecast the NG outlet temperature. The endeavor to create an effective prediction model faced specific challenges, primarily due to the narrow operational range of fan speeds and safety-focused guidelines. To surmount these obstacles, various learning algorithms were evaluated under multiple conditions. Ultimately, a DNN model exhibiting lower values of both absolute mean error (MAE) and mean square error (MSE) was successfully established.

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Section: Introductionmentioning

confidence: 99%