INVESTIGADORES
BANDONI Jose Alberto
congresos y reuniones científicas
Título:
SIMULTANEOUS PROCESS DESIGN AND CONTROL FOR OPTIMAL GRADE TRANSITION IN A STYRENE POLYMERIZATION REACTOR
Autor/es:
M. ASTEASUAIN, A. BRANDOLIN, C. SARMORIA, A. BANDONI
Lugar:
Valencia, España
Reunión:
Congreso; SLAP 2004; 2004
Resumen:
SIMULTANEOUS PROCESS DESIGN AND CONTROL FOR OPTIMAL GRADE
TRANSITION IN A STYRENE POLYMERIZATION REACTOR
Mariano Asteasuain, Adriana Brandolin, Claudia Sarmoria, Alberto Bandoni
PLAPIQUI, UNS-CONICET, CC 717, (8000) Bahía Blanca, Argentina.
Grade transition optimization is very important for a
profitable operation of continuous polymerization
processes. Polymers have many application areas, and
each area demands different grade specifications. In
order to achieve market requirements, continuous
polymer plants usually alternate between the
production of several polymer grades in the same
equipment by switching the operating points. Since this
operation can be performed even every few days, it is
essential to determine optimal transition policies that
minimize the production of off-specification material
and the transition time. In addition to this, it is
necessary to count with a suitable control system that
guarantees that the optimal transition policies are
actually followed, and also ensures safe process
operation.
This has motivated intense research on grade transition
optimization and control of polymerization reactors
(1). In most cases, however, a sequential approach has
been used to deal with control system design and
optimization of transition policies. By following this
sequential methodology, the strong interaction between
process design and control is not accounted for.
Although several works in other fields of chemical
engineering have shown the great benefits of
incorporating control aspects in the process design
stages (2), few efforts have been done in this direction
in polymer engineering (3).
In this work we focus on grade transition operation in a
CSTR styrene polymerization reactor. A Mixed
Integer Dynamic Optimization (MIDO) approach is
used to simultaneously design the process and its
control system for A ↔ B grade transition sequences.
The process design involves reactor size and initiator
type selection (discrete decisions), and the two steady
state operating points in which each polymer grade is
produced (continuous decisions). Simultaneously,
feedforward feedback controllers are optimally
designed to drive the process between steady states.
The proposed control superstructure for PI feedback
controllers involves the monomer, initiator and coolant
flow rates as possible manipulated variables, and the
reactor temperature, jacket temperature, number
average molecular weight and polymerization rate as
possible controlled variables. Optimal pairings
between them must be determined (discrete decisions)
as well as the controllers tuning parameters and the set
points of the controlled variables (continuous
decisions). The feedforward controllers signals consist
of time-varying profiles for each manipulated variable,
which are calculated by the optimizer.
The MIDO was solved with the gPROMS/gOPT v2.2.6
package (Process Systems Enterprise Ltd.). A few
examples of the optimal design features are shown in
Figures 1 and 2. Figure 1 presents the optimal feedback
control structure for the A → B transition. All allowed
loop pairings are used in this structure. The
combination of feedback loops is probably intended to
compensate for the strong interaction between process
variables in a polymerization system.
Figure 2 shows the Mn trajectory for the same
transition. It can be seen that the optimal transition
policy implemented by the control system takes only
20 min to reach grade B specification (in a reactor with
a residence time of 2.5 h), and closely follows this
value thereon. The optimal time profile of the Mn set
point resulted in a constant value coincident with Mn↔ B grade transition sequences.
The process design involves reactor size and initiator
type selection (discrete decisions), and the two steady
state operating points in which each polymer grade is
produced (continuous decisions). Simultaneously,
feedforward feedback controllers are optimally
designed to drive the process between steady states.
The proposed control superstructure for PI feedback
controllers involves the monomer, initiator and coolant
flow rates as possible manipulated variables, and the
reactor temperature, jacket temperature, number
average molecular weight and polymerization rate as
possible controlled variables. Optimal pairings
between them must be determined (discrete decisions)
as well as the controllers tuning parameters and the set
points of the controlled variables (continuous
decisions). The feedforward controllers signals consist
of time-varying profiles for each manipulated variable,
which are calculated by the optimizer.
The MIDO was solved with the gPROMS/gOPT v2.2.6
package (Process Systems Enterprise Ltd.). A few
examples of the optimal design features are shown in
Figures 1 and 2. Figure 1 presents the optimal feedback
control structure for the A → B transition. All allowed
loop pairings are used in this structure. The
combination of feedback loops is probably intended to
compensate for the strong interaction between process
variables in a polymerization system.
Figure 2 shows the Mn trajectory for the same
transition. It can be seen that the optimal transition
policy implemented by the control system takes only
20 min to reach grade B specification (in a reactor with
a residence time of 2.5 h), and closely follows this
value thereon. The optimal time profile of the Mn set
point resulted in a constant value coincident with Mn→ B transition. All allowed
loop pairings are used in this structure. The
combination of feedback loops is probably intended to
compensate for the strong interaction between process
variables in a polymerization system.
Figure 2 shows the Mn trajectory for the same
transition. It can be seen that the optimal transition
policy implemented by the control system takes only
20 min to reach grade B specification (in a reactor with
a residence time of 2.5 h), and closely follows this
value thereon. The optimal time profile of the Mn set
point resulted in a constant value coincident with MnMn trajectory for the same
transition. It can be seen that the optimal transition
policy implemented by the control system takes only
20 min to reach grade B specification (in a reactor with
a residence time of 2.5 h), and closely follows this
value thereon. The optimal time profile of the Mn set
point resulted in a constant value coincident with MnMn set
point resulted in a constant value coincident with MnMn
of grade B polymer. Optimal values for the other
process and control system deign features mentioned
before were also calculated.
Equivalent results have been obtained for the B → A
transition sequence.→ A
transition sequence.
ACKNOWLEDGEMENTS
The authors thank CONICET and UNS for financial support.
REFERENCES
(1) Embiricu, M., E.L. Lima and J.C. Pinto (1996). Polym. Eng. Sci.,
36(4), 433-447.
(2) Bansal, V., J.D. Perkins and E.N. Pistikopoulos (2002). Ind. Eng.
Chem. Res., 41, 760-778.Polym. Eng. Sci.,
36(4), 433-447.
(2) Bansal, V., J.D. Perkins and E.N. Pistikopoulos (2002). Ind. Eng.
Chem. Res., 41, 760-778.Ind. Eng.
Chem. Res., 41, 760-778., 41, 760-778.
Fig. 1: Optimal pairings between manipulated and controlled
variables in A → B grade transition.→ B grade transition.
Coolant flow rate
Initiator flow rate
Monomer flow rate
Reactor temperature
Jacket temperature
Number average
molecular weight
Polymerization rate
Fig. 2: Mn and Mn set point profiles in A → B grade
transition.Mn and Mn set point profiles in A → B grade
transition.
0 40 80 120 160 200
Time (min)
35000
40000
45000
50000
55000
Mn (g/mol)
Polymer Mn
Mn set point
Mn of Grade A
Mn of Grade B
(3) Chatzidoukas, C., J.D. Perkins, E.N. Pistikopoulos, and C.
Kiparissides (2003). Chem. Eng. Sci., 58, 3643-3658.Chem. Eng. Sci., 58, 3643-3658.