(652e) Optimal Measurement Selection for Controlled Variables for Kaibel Distillation Column

Control structure selection
plays vital role to operate the process plants at optimum. The decision on
which variables to be controlled, which variables to be measured, which inputs
to be manipulated and which links should be made between them are called
control structure selection. Generally control structural selection is done
based on heuristics or intuition of process engineers. This makes it difficult
to compare different control structures and improve the proposals. Skogestad
and coworkers (Skogestad, 2000, Morari et al. 1980) have proposed to use the
steady state process model to find ?self-optimizing? variables with an
assumption that plant economics are governed by the pseudo/steady state
behavior. The idea of ?self- optimizing control? can be defined as suitable
selection of c's and by keeping
these CVs (c's) at constant set
points, the operation gives acceptable steady state loss from the optimal
operation even in the presence of disturbances. In this framework, we seek to
find the optimal controlled variables, c
= Hy (y are the measurements) as
combinations of fewer/all the measurements. The objective here is to find the
combination matrix H. Here we
briefly present the methods of Mixed Integer Quadratic Programming methodology
to (i) select the optimal individual measurements; (ii) select the optimal fewer
measurements and (iii) handle few structural constraints that result in minimal
loss (Yelchuru and Skogsetad, 2011) from optimal operation.

  A 4 product Kaibel column has high energy
saving potential (Halvorsen and Skogestad, 2003), but is a difficult control
problem with limited degrees of freedom compared to a conventional distillation
sequence for 4 product separation. This case study is an interesting example
for the demonstration of the proposed systematic procedure to select the
control variables as individual measurements or combinations of fewer
measurements with measurements from different sections of the column, as it
highlights importance of structural constraints feature of the method. Structural
constraints are important for dynamic considerations, for example, at least one
temperature in the prefractionator should be controlled (Strandberg and
Skogestad, 2006) in the regulatory layer. 

References

I. J.
Halvorsen and S. Skogestad (2003). "Minimum
Energy Consumption in Multicomponent Distillation. 3.
More than Three Products and Generalized Petlyuk Arrangements."
Industrial & Engineering Chemistry Research 42(3): 616-629.

M. Morari, G.
Stephanopoulos, and Y. Arkun (1980). Studies in the synthesis
of control structures for chemical processes. part
i: formulation of the problem. Process decomposition and the
classification of the control task. Analysis of the
optimizing control structures. AIChE
Journal, 26(2):220?232.

S. Skogestad (2000).
Plantwide control: The search for the self-optimizing control structure. Journal of Process Control,
10:487?507.

J. Strandberg and S. Skogestad (2006). Stabilizing operation of a
4-product integrated Kaibel column. Institution of Chemical Engineers Symposium Series, Institution of
Chemical Engineers; 1999. 152:
636-647.

R. Yelchuru and S.
Skogestad (2011). Optimal controlled variable selection for individual process
units in self optimizing control with miqp formulation. In accepted for American Control Conference,
SanFrancisco, USA.