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Lim C Siang

A novel automated soft sensor design tool for industrial applications based on machine learning
Machine learning for real-time green carbon dioxide tracking in refinery processes
Real-time tracking of renewable carbon content with AI-aided approaches during co-processing of biofeedstocks
Interpretable Industrial Soft Sensor Design Based on Informer and Shap
Interpretable Soft Sensors using Extremely Randomized Trees and Shap
Causal discovery based on observational data and process knowledge in industrial processes
Soft Sensor Change Point Detection and Root Causal Analysis
Tracking the green coke production when co-processing lipids at a commercial fluid catalytic cracker (FCC): combining isotope 14 C and causal discovery analysis

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