(647e) Simultaneous Design, Control and Operational Optimisation of a Domestic CHP System

In
process systems engineering, the design of economically profitable
and operationally optimal processes has been an active area of
research for over 25 years. Significant contributions have been made
throughout the years that consider the problem through a variety of
approaches, particularly the consideration of (i) operability and
design under uncertainty [1] as well as (ii) the system
controllability with regulatory control structure selection and its
tuning [2, 3] and (iii) the design and model predictive control with
stability and robustness aspects taken into account, in a unified
framework [4]. During the last few years there has also been great
interest in the field of optimal operation, with several studies in
literature exploring the interactions between shorter term
operational decisions (regulatory and model-based control) with their
longer term counterparts (scheduling and planning techniques) thus
attempting to bridge the gap between the two [5-8]. The aim of this
approach targets the long-term optimal operation of a process while
taking into account its control aspects and the inherent handling of
uncertainty [9].

In this work we present a study where the
interactions of all three aspects of decision making of the process --
from the design aspects of the process and control elements to the
optimal scheduling and control strategies -- in a single framework
are considered, through its application on a series on micro-CHP
units destined for domestic use. We consider the design aspects of
the process as uncertain during the design procedure of the
model-based controllers as well as the rolling-horizon scheduling.
The latter takes into account the dynamics of the system through the
consideration of the interactions between the control and scheduling
via the use of a bridging model [5]. The integrated system of control
and scheduling is solved into a multi-parametric fashion, thus
allowing the acquisition of the exact solution of the problem a
priori, while considering both (i) the design aspects, and (ii) the
operational aspects of the process as uncertain. The optimally
operational process is then introduced to dynamic optimisation which
targets the design optimisation.

The principles introduced in [10] form the
basis for this work. More specifically, a high fidelity model of an
internal combustion engine equipped, natural gas powered, domestic
CHP plant has been developed and introduced into the gPROMS®
simulation and optimisation environment [11]. The model is able to
simulate the simultaneous production of electrical power and usable
heat. Aspects of the model such as the size of the internal
combustion engine as well as a hot water buffer tank are considered
for the optimal design. The model is subjected into approximation
techniques in order for simplified state-space models with minimal
loss of accuracy to be acquired. The approximation technique involves
the use of the System Identification Toolbox of MATLAB®. Two
subsystems are identified which reflect into the inherent distinct
operation of the CHP -- the power generation subsystem and the heat
recovery subsystem. The approximation procedure takes into account
the design variables of the high fidelity model. The design of the
mp-MPCs is based on the afore-mentioned state space models thus
resulting into a design dependent decentralized control scheme [12].
The principles described in [13] are followed for the design of the
rolling-horizon scheduling approach. In this case, the design aspects
of the process are also treated as uncertainty. Finally, the design
dependent bridging model is developed that closes the gap between the
short-term and long-term operation of the process. All three
rolling-horizon policies are introduced into the gPROMS® environment
via the development of a software tool described extensively in [10].
Figure 1 graphically represents the set-up of the system and its
components.

Figure
1 CHP System setup for domestic use with design uncertainty

At
this point, the system can be simulated for given horizons, for a
range of designs. The system is then introduced into the gOPT®
optimisation functionality of gPROMS® that targets the design
optimisation. At every iteration of the single vector shooting
optimisation procedure, the design of the system is explored for a
predefined demand profile, thus causing the uncertainty to realize
itself. The operational objective of the design-dependent control is
minimal mismatch to a set-point which is provided by the economically
optimal solution of the design-dependent rolling-horizon scheduling
and bridging model. The dynamic design optimisation targets the cost
of acquisition of the system components via the transition of the
cost on a daily basis, given a 10-year loan repayment.

Through this technique, we manage to target
all three aspects of designing economically and operationally optimal
processes with the use of a high-fidelity model and design-dependent
rolling-horizon strategies. Most importantly though, we manage to
provide a framework that can be applied to a large variety of
processed without compromising the accuracy that a high-fidelity
model provides and at the same time using advanced control and
scheduling techniques in a closed-loop fashion.

References

1. van Schijndel, J.M.G. and E.N.
Pistikopoulos, Towards the integration of process design, process
control and process operability - Current stares and furore trends.
Foundations of Computer-Aided Process Design, 2000. 96: p. 99--112.

2. Mohideen, M.J., J.D. Perkins, and E.N.
Pistikopoulos, Optimal design of dynamic systems under uncertainty.
AIChE Journal, 1996. 42(8): p. 2251--2272.

3. Bansal, V., et al., Simultaneous design
and control optimisation under uncertainty. Computers & Chemical
Engineering, 2000. 24(2�7):
p. 261--266.

4. Sakizlis, V., J.D. Perkins, and E.N.
Pistikopoulos, Parametric Controllers in Simultaneous Process and
Control Design Optimization. Industrial & Engineering Chemistry
Research, 2003. 42(20): p. 4545--4563.

5. Baldea, M. and I. Harjunkoski,
Integrated production scheduling and process control: A systematic
review. Computers & Chemical Engineering, 2014. 71: p. 377--390.

6. Zhuge, J. and M.G. Ierapetritou,
Integration of scheduling and control for batch processes using
multi-parametric model predictive control. AIChE Journal, 2014.
60(9): p. 3169--3183.

7. Subramanian, K., C.T. Maravelias, and
J.B. Rawlings, A state-space model for chemical production
scheduling. Computers & Chemical Engineering, 2012. 47(0): p.
97--110.

8. Engell, S. and I. Harjunkoski, Optimal
operation: Scheduling, advanced control and their integration.
Computers and Chemical Engineering, 2012. 47: p. 121-133.

9. Patil, B.P., E. Maia, and L.A.
Ricardez-Sandoval, Integration of scheduling, design, and control of
multiproduct chemical processes under uncertainty. AIChE Journal,
2015. In press

10. Pistikopoulos, E.N., et al., PAROC-An
integrated framework and software platform for the optimisation and
advanced model-based control of process systems. Chemical Engineering
Science, 2015.

11. Diangelakis, N.A., C. Panos, and E.N.
Pistikopoulos, Design optimization of an internal combustion engine
powered CHP system for residential scale application. Computational
Management Science, 2014. 11(3): p. 237--266.

12. Diangelakis, N.A. and E.N.
Pistikopoulos, A Decentralised Multi-parametric Model Predictive
Control Study for a Domestic Heat and Power Cogeneration System, in
Computer Aided Chemical Engineering2015. Accepted for publication

13. Kopanos, G.M. and E.N. Pistikopoulos,
Reactive scheduling by a multiparametric programming rolling horizon
framework: A case of a network of combined heat and power units.
Industrial and Engineering Chemistry Research, 2014. 53(11): p.
4366-4386.