Lahiri, S. K.
Development of support vector regression-based soft sensor
Petrochemicals
/ Process Control
/ Alcohols
/ Americas
/ Analyzers
/ Asia/Pacific
/ Engineering & Design
/ Europe
/ Information Systems
/ Middle East
Application was used in a commercial ethylene glycol plant
A support vector classification method for regime identification of slurry transport in pipelines
Statistical analysis showed the proposed solution has an average misclassification error of only 1.5%
Computational fluid dynamics simulation of solid–liquid slurry flow
The resulting model's predictions showed reasonably good agreement with the experimental data
Genetic algorithm tuning improves artificial neural network models
The technique is illustrated by predicting hold-up of slurry flow in pipelines
Minimize power consumption in slurry transport
Accurately predict critical velocity
Process modeling and optimization strategies integrating neural networks and differential evolution
The technology was applied to an ethylene oxide reactor
Novel approach for process plant monitoring
Using statistical data compression important process changes can be quickly detected and identified
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Construction Boxscore: Project Spotlight
Project:
Long Son Petrochemicals Complex
Location:
Long Son, Vietnam
Operator:
Siam Cement Group
Cost:
$5.4 B
Capacity:
1.65 MMtpy
Completion date:
2023
Status:
Under Construction
