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Phantisa Limleamthong. A LSTM-based utility consumption prediction for a process with performance degradation : a case study of a vinyl chloride monomer plant. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2021.
A LSTM-based utility consumption prediction for a process with performance degradation : a case study of a vinyl chloride monomer plant
Name: Chutithep rochpuang
Organization :
Kasetsart University. Faculty of Engineering
Email :
Chutithep.roc@ku.th
Name: Chanin Panjapornpon
Organization :
Kasetsart University. Faculty of Engineering
Email :
fengcnp@ku.ac.th
Name: Phantisa Limleamthong
Organization :
Kasetsart University. Faculty of Engineering
Email :
Phantisa.l@ku.ac.th
keyword:
Vinyl chloride monomer industry
LCSH:
Petroleum chemicals
Abstract:
Energy efficiency is essential to minimize greenhouse gas emissions and energy costs. Proper operating conditions can be applied to reduce excess utility consumption in processes. Petrochemical processes are complex systems with recycle streams, reactions, and phase separation, making it difficult to calculate utility consumption using first-principles models. In addition to this complexity, such processes often experience performance degradation, for example, coking in thermal cracking furnaces. This research proposes a utility prediction model to identify optimal utility consumption when a process operates under a production plan while accounting for performance degradation in unit operations. The proposed model is a data-driven artificial neural network. To characterize process dynamic behavior, long short-term memory (LSTM) networks are implemented. LSTM is an advanced recurrent neural network with feature extraction capability via memory cells. A vinyl chloride monomer (VCM) process is used as a case study. The VCM process was simulated using the UniSim Design simulator, and time-series data were generated through a co-simulation environment between MATLAB and UniSim Design. The model prediction performance was evaluated by comparing predicted utility consumption with the production plan in UniSim Design.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2021
Modified:
2026-02-03
Issued:
2026-02-03
บทความ/Article
application/pdf
BibliograpyCitation :
English In Thai Institute of Chemical Engineering and Applied Chemistry. The 30th Thai Institute of Chemical Engineering and Applied Chemistry Conference (TIChE 2021) (ESD07). Nakhon Ratchasima : Suranaree University of Technology
eng
©copyrights King Mongkut's University of Technology North Bangkok
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0.028319 วินาที
Chutithep rochpuang
| Title | Contributor | Type |
|---|---|---|
| Utility consumption prediction for vinyl chloride monomer process with a performance degradation in a cracking furnace
มหาวิทยาลัยเกษตรศาสตร์ Chutithep Rochpuang | Chanin Panjapornpon | วิทยานิพนธ์/Thesis |
| A LSTM-based utility consumption prediction for a process with performance degradation : a case study of a vinyl chloride monomer plant
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chutithep rochpuang;Chanin Panjapornpon;Phantisa Limleamthong | บทความ/Article |
Chanin Panjapornpon
| Title | Contributor | Type |
|---|---|---|
| The Monitoring of water content in PEMFC via state observer technique
มหาวิทยาลัยเกษตรศาสตร์ Chanin Panjapornpon;Chinnavat Thawornkuno | Kasetsart University, Bangkok (Thailand). Faculty of Engineering. Research & Academic Service Division | บทความ/Article |
| A coupled pH and level control in pH process by data-driven based, input-output linearization
มหาวิทยาลัยเทคโนโลยีมหานคร Sirawich Lohprasert;Chanin Panjapornpon | บทความ/Article | |
| A LSTM-based utility consumption prediction for a process with performance degradation : a case study of a vinyl chloride monomer plant
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chutithep rochpuang;Chanin Panjapornpon;Phantisa Limleamthong | บทความ/Article |
Phantisa Limleamthong
| Title | Contributor | Type |
|---|---|---|
| Assessing sustainability performance of Thailand provinces via super efficiency data envelopment analysis
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Narawit Yingyong;Satawat Thumwong;Phantisa Limleamthong | บทความ/Article | |
| Machine learning for predictive modelling of hydrogen production from biomass gasification
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| Enhancing non-catalytic biomass pyrolysis yield predictions using machine learning and data augmentation
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Anchalee Chaichanavongsaroj;Dasakorn Rungrampan;Phantisa Limleamthong | บทความ/Article | |
| A LSTM-based utility consumption prediction for a process with performance degradation : a case study of a vinyl chloride monomer plant
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chutithep rochpuang;Chanin Panjapornpon;Phantisa Limleamthong | บทความ/Article |