(28c) Evolutionary Optimization Environment for Power Plant Control with Dynsim® Interface

Evolutionary
Optimization Environment for Power Plant Control with Dynsim^®
Interface

Ghassan Al-Sinbol, Mario G. Perhinschi, Debangsu
Bhattacharyya

            Due to the increasing complexity and
multi-dimensionality of modern power plants, selecting and optimizing the architecture
of control systems within a plant-wide control strategy is a challenging task.  The control problems to be solved are highly
nonlinear, multidimensional, constrained, and prone to multiple extrema.  Therefore, optimal analytical or numerical
solutions are in most cases impossible or impractical.  For such problems, evolutionary algorithms can
provide effective solutions.  Recent efforts
have identified the artificial immune system (AIS) paradigm as a promising
source for mechanisms that can enhance more common evolutionary algorithms and
mitigate some of their drawbacks.  The
development and use of complex power plant simulation environments, such as Dynsim^®,
is currently receiving significant attention for a variety of applications.  However, their use for control system design
and optimization has yet to reach full potential.  To synergistically merge the capabilities of
evolutionary algorithms and the availability of advanced process simulation
software, an interactive computational
environment for the optimization of modern power plant control using
evolutionary techniques is presented in this paper.

            The proposed optimization algorithms
are implemented in Matlab^®, while the power plant may be modeled in
Matlab^®/Simulink^® or Dynsim^®.  The overall proposed architecture for control
optimization including the interface between these computational environments
is aimed at facilitating reaching the full potential of advanced methodologies
for power plant control system design and optimization.

            The general optimization scenario
and specific algorithms and parameters are setup by the user through Matlab^®
graphical user interface menus.  The
object linking and embedding for process control data access protocol is used to
facilitate variable exchange between Matlab^® and Dynsim^®.  The interface integrates Matlab^®
based models and controllers into Dynsim^® models.  Dynsim^® feature “Scenarios” is used
to facilitate further the communication.

            The optimization scenario includes
two options: a baseline genetic algorithm and an immunity enhanced evolutionary
algorithm.  The modular architecture of
the computational environment allows for the addition of new algorithms into a central
library and testing and evaluation of newly developed approaches.  The baseline genetic algorithm implemented
within the optimization environment features a standard architecture with
randomly generated initial population, integer-based chromosome representation,
roulette-wheel selection technique and elitist strategy, gene mutation, and
single-point crossover.  Immunity-inspired
enhancements include seeding and vaccination of initial population,
hyper-mutated cloning, selection, and population diversification based on
affinity to self/non-self.  These algorithms are expected to enhance the
computational effectiveness, improve convergence, be more efficient in handling
multiple local extrema, and achieve adequate balance between exploration and
exploitation.

            Two major categories of optimization
problems can be addressed: optimization of constant control system parameters (controller
gains) and optimization of variable setpoints. 
For the first type of problem, the solution is defined as the set of best
performing controller gains, while for the second, the solution consists of a
vector of optimal setpoints over pre-defined time intervals. 

            The functionality of the proposed optimization computational environment
is illustrated through optimizing the control laws for an acid gas removal unit
as part of an integrated gasification combined cycle plant.  The unit selectively removes H₂S
and CO₂ using SELEXOL solvent. 
The unit consists of numerous equipment items including trayed and packed distillation columns, pressure vessels,
heat exchangers, pumps, and compressors. 
These components are strongly coupled through mass and heat exchange
resulting in a challenging control problem.

            The general architecture of the evolutionary
optimization environment is illustrated in Figure 1.

Figure 1.  General Architecture of the Evolutionary
Optimization Environment