The
present study proposes a new production planning framework
that considers the effect of closed-loop control and process disturbances/disruptions.
The production planning layer is augmented to include process control
degrees of freedom, process operating constraints, operating uncertain
parameters (related to closed-loop feedback), and disturbance information.
This results in a stochastic production planning problem, whose solution
successfully reduces the economic gap between production planning
predictions and realized production. The proposed approach is applied
to a large-scale model of a refinery section comprising a fluid catalytic
cracker (FCC) and a fractionator. Extensive simulations involving
different disturbances, operating modes, models, and production planning
formulations are performed. The results demonstrate the benefits of
the proposed framework in terms of economic performance, computational
tractability, and ease of application. Finally, the impact of the
economic model and plant-model mismatches between production planning
and model predictive control (MPC), which arise in practice, is investigated.
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