PTQ Q3 2022 Issue

LCO

BBs

Slurry

PPs

1400

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0.075 0.050 0.025 0.000

Cat gas

Dry gas

Cat gas octane

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0.100 0.025 0.050 0.075

Figure 3 Bimodal CLNN gains between ROT and FCC product yields illustrating the learned shift in product yields with a new catalyst formulation during CLNN process model training

predictions of control variables to then proactively use key handles to increase the objective function based on the refiner’s defined price deck. Decisions are sometimes different from what is defined by traditional steady-state approaches as the CLNN understand non-linearity and changes in the process state by continuously leveraging their knowledge of the FCC

dynamics. For example, the CLNN chose to cut feed rather than maximise feed in the given state, making room for an ROT increase while respecting the key operating limits to increase the objective function significantly (see Figure 4 ). Prior to CLNN, operators and process engineers were forced to change bounds in APC to conservatively stay within operating limits as the process response to

MV: Total feed rate

Objective function

+$40k/day

MV: ROT

CV: Regen slide valve dP

Upper bound

MV: Resid feed

Reactor-regen dP

Lower bound

CLNN engaged

PV signal

MV setpoint

Bound

Figure 4 Prior to CLNN, APC bounds and MV targets were manually adjusted to maintain targets and constraints. When engaged, CLNN drives the MVs dynamically to increase the objective function while respecting operational constraints

88

PTQ Q3 2022

www.digitalrefining.com

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