Neural network models for atmospheric residue desulfurization using real plant data with novel training strategies-Reference-Cited by-同舟云学术

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Neural network models for atmospheric residue desulfurization using real plant data with novel training strategies

Published:2023-09 Issue: Volume:177 Page:108333
ISSN:0098-1354
Container-title:Computers & Chemical Engineering
language:en
Short-container-title:Computers & Chemical Engineering

Author:

Jung Yungun,Kim Hyungjun,Jeon Gyeonggwan,Kim Yeonsoo^ORCID

Funder

Korea Agency for Infrastructure Technology Advancement

Ministry of Trade, Industry and Energy

Ministry of Land, Infrastructure and Transport

Korea Institute of Energy Technology Evaluation and Planning

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference34 articles.

1. A review of methods for the demetallization of residual fuel oils;Ali;Fuel Process. Technol.,2006

2. Application of artificial neural network for internal combustion engines: A state of the art review;Bhatt;Arch. Comput. Methods Eng.,2022

3. Forecasting industrial aging processes with machine learning methods;Bogojeski;Comput. Chem. Eng.,2021

4. Predictive modeling of large-scale integrated refinery reaction and fractionation systems from plant data, part 1: Hydrocracking processes;Chang;Energy Fuels,2011

5. Using a neural network to predict deviations in equilibrium model of co2 capture by absorption with potassium carbonate;de Vasconcelos;Comput. Chem. Eng.,2023

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1. A data enhancement method based on generative adversarial network for small sample-size with soft sensor application;Computers & Chemical Engineering;2024-07

2. The adsorption routes of 4IR technologies for effective desulphurization using cellulose nanocrystals: Current trends, challenges, and future perspectives;Heliyon;2024-01

3. Multi-Stage Approach Using Convolutional Triplet Network and Ensemble Model for Fault Diagnosis in Oil Plant Rotary Machines;Machines;2023-11-06

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