Deep Learning and AutoML for Dynamic Modeling of LNG Regasification Process Using Seawater-Reference-Cited by-同舟云学术

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

Published:2021 Issue: Volume: Page:1617-1622
ISSN:1570-7946
Container-title:31st European Symposium on Computer Aided Process Engineering
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Author:

Shin Yongbeom,Shin Dongil

Funder

Ministry of Trade, Industry and Energy

Korea Institute for Advancement of Technology

Korea Institute of Energy Technology Evaluation and Planning

Publisher

Elsevier

Reference6 articles.

1. Thermal performance calculation with heat transfer correlations and numerical simulation analysis for typical LNG open rack vaporizer;Cheng;Applied Thermal Engineering,2019

2. Decision-making tools for a LNG regasification plant siting;D’alessandro;Journal of Loss Prevention in the Process Industries,2016

3. Simulation and performance analysis of a heat transfer tube in SuperORV;Jin;Cryogenics,2014

4. First Principle Modeling and Neural Network-Based Empirical Modeling with Experimental Validation of Binary Distillation Column;Singh;Chemical Product and Process Modeling,2013

5. Numerical Simulation of Gasification Process on Rib-tube of Open Rack Vaporizer;Su;Journal of Computers,2014

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