An improved iterative real-time optimization scheme for slow processes
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Date
2016-11-16
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Abstract
Iterative Real-Time Optimization (RTO) has gained increasing attention in
the context of model-based optimization of the operating points of chemical
plants in the presence of plant-model mismatch. In all these schemes, it is
necessary to wait for the plant having reached a steady-state to obtain the
required information on plant performance and constraint satisfaction, which
leads to slow convergence in the case of processes with slow dynamics. This
works addresses this issue by considering both parametric, and structural
plant-model mismatch. First, a simple approach to determine the type of
plant-model mismatch with the use of transient data is discussed. An approach
for dealing with parametric mismatch based on a sensitivity analysis of the
nominal dynamic model is presented, and its performance is evaluated with
the case-study of a Continuously Stirred Tank Reactor (CSTR), where fast
convergence to the optimum can be obtained, even with noisy measurements.
For the case of structural mismatch, nonlinear system identification is integrated
with iterative RTO. The identified models are used to predict the
steady-state of the system, thus reducing the total optimization time. The
performance of the strategy is illustrated by simulation studies of a CSTR and
a hydroformylation process. It is shown that a mixed scheme, where both a
linear and nonlinear model are used for steady-state prediction, results in fast
convergence to a neighborhood of the true optimum, even in the presence of
measurement noise. The use of taylored nonlinear models for dynamic system
identification is shown to be a promising approach for reducing the time
necessary to reach the optimum of a process.