Process Digital Twins of Furnace
nace
Cognitive Furnace4.0
Furnace
Furnace Optimiser
Furnace E ciency
e
Feed HC
nce alysis)
HC preheat H13
Zone
al model mic model del
Feed DS
1
1
Convection section
Transfer line exchanger
PTLE
PTLE
Air
Burner cell A Coil
Coil
Radiant section
Cracking gas
2
-time
Burner cell B
CG
ontrol
Fuel gas
2
to increase furnace efficiency and productivity, as well as reduce NOx and CO 2 emissions. A Process-AI Platform with out-of-the-box features: • Core AI process analytics module: Module for data pre-processing suitable for major process industries for statistical analysis. • AI/ML custom module : AI models are built using industrial-grade data simulated from plant
the atmosphere. Coke formation also leads to reduced furnace run length. Addressing fuel composition changes by leveraging the real-time thermodynamics model and AI/ML techniques enables continuous bias of the air/fuel ratio to stabilise combustion and heat transfer into the tubes. To address these issues, we have leveraged the Cognitive Furnace4.0 platform for data
acquisition from DCS/ historians in real-time, pre- processing and modelling based on first principles and machine learning algorithms. LivNSense has also levered its unique IP - disturbance index for estimating manufacturing condition variations in real-time. The outcome is delivered through live furnace digital twins for continuous improvement and high accuracy. Our holistic platform has also been proven
0.9000 0.6000 0.6500 0.7000 0.7500 0.8000 0.8500
Actual CPR Optimised CPR predicted
Coil pressure ratio (CPR) trends showing increased furnace run length (hours)
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