Optimal operation strategies for dynamic processes under uncertainty
Abstract
The main focus of this thesis is to find implementation strategies for the optimal
operation of processes during transients. That is, we do not focus on
the algorithms to solve a given dynamic optimization problem, but on how
to implement the solution in practice using control. The underlining theme
is based on the general idea of finding feedback policies that give acceptable
performance even in the presence of model uncertainties and disturbances.
By 'acceptable' we mean that important constraints should always be satisfied and the economic performance should be near the optimal. In this
thesis we considered different classes of applications, each one with their
own particularities and challenges.
The first part of the work deals with the optimal operation of thermal
energy storage systems. We consider the optimal operation of energy storage
in buildings with focus on the optimization of an electric water heating
system. The optimization objective is to minimize the energy costs of heating
the water, with the requirement that we should satisfy the uncertain
demand at any time. The main complications in this problem are the time
varying nature of the electricity price and the unpredictability of the future
water demand. First, we present a detailed problem formulation which
may also be suitable similar problems. Many insights into the optimization
problem formulation are given and guidelines on implementation strategies
including feedback control structures are proposed.
Next, we use the hot water system as an example to illustrate our proposed
implementation strategy based on hierarchical decomposition of the
optimization-control problem. In our approach, economic objectives and
control objectives are decoupled using a two-layer cascade feedback structure.
We show that the decomposed optimization problem can be written
as a simple linear program (LP) which can be solved very efficiently. The
main result is that great economical benefits can be obtained at a very low
computational cost and suitable for low cost embedded hardware.
Part two of the thesis is dedicated to an intelligent anti-slugging control system for o
shore oil production maximization. Existing anti-slug
control systems are not robust and tend to become unstable after some
time, because of inow disturbances or plant dynamic changes, thus, requiring
constant supervision and retuning. A second problem is the fact
that the ideal setpoint is unknown and we could easily choose a suboptimal
or infeasible operating point. Here we present a method to tackle these
problems. Our complete control solution is composed of an autonomous
supervisor that seeks to maximize production by manipulating a pressure
setpoint and a robust adaptive controller that is able to quickly identify and
adapt to changes in the plant. Our proposed solution has been tested in a
experimental rig and the results are very encouraging. An analysis of the
robustness and optimality of different linear controllers for slug mitigation
is also carried out in this part of the thesis.
In the last part of the thesis we discuss near-optimal operation strategies
using simple feedback control. First, we generalize the neighbouring extremal
control design that has been presented in the literature (Gros et al.,
2009b) to explicitly handle measurement noise and implementation errors.
The bene ts of our method are illustrated in a case study where we show
that the sensitivity of the controller performance to measurement noise is
considerably reduced. Finally, we extended the concept of self-optimizing
control (Skogestad, 2000; Alstad and Skogestad, 2007) for the near-optimal
operation of transient processes. The main idea is to find a function of the
measurements whose trajectory is optimally invariant to disturbances and
then track the trajectory using standard feedback controllers. Doing so results
in near-optimal economic operation in spite of disturbances without
the need for re-optimization. We show that the invariant trajectories can
be computed as linear combinations of the measurement vector, where the
time-varying combination matrix is easily obtained from optimal sensitivities.
Has parts
Paper 1: V. de Oliveira, J. Jãschke and S. Skogestad, Optimal operation of energy storage in buildings: Use of the hot water system. Journal of Energy Storage Volume 5, February 2016, Pages 102–112 http://dx.doi.org/ 10.1016/j.est.2015.11.009Paper 2: V. de Oliveira, J. Jãschke and S. Skogestad, Hierarchical control in dynamic optimization of energy storage systems
Paper 3: V. de Oliveira, J. Jãschke and S. Skogestad, Null-space method for optimal operation of transient processes. - Presented at IFAC International Symposium on Dynamics and Control of Process Systems, 2016. © 2016 IFAC.
Paper 4: V. de Oliveira, J. Jãschke and S. Skogestad, Neighbouring-Extremal Control for Process Optimization Using Noisy Measurements. International Symposium on Advanced Control of Chemical Processes, IFAC-PapersOnLine 2015 ;Volum 48.(8) s. 698-703 http://dx.doi.org/10.1016/j.ifacol.2015.09.050 © 2015 IFAC.
Paper 5: V. de Oliveira, J. Jãschke and S. Skogestad, An autonomous ap- proach for driving systems towards their limit: an intelligent adaptive anti-slug control system for production maximiza- tion. 2nd IFAC Workshop on Automatic Control in O shore Oil and Gas Production, IFAC-PapersOnLine 2015 ;Volum 48.(6) http://dx.doi.org/10.1016/j.ifacol.2015.08.017 © 2015 IFAC.
Paper 6: E. Jahanshahi, V. de Oliveira, C. Grimholt and S. Skogestad, A comparison between Internal Model Control PIDF, optimal PIDF and robust controllers for unstable ow in risers, IFAC World Congress, 2014. IFAC Proceedings series 2014 ;Volum 19. s. 5752-5759 http://dx.doi.org/10.3182/20140824-6-ZA-1003.02381 © 2014 IFAC.
Paper 7: V. de Oliveira, J. Jãschke and S. Skogestad, Dynamic online opti- mization of a house heating system in a uctuating energy price scenario, IFAC International Symposium on Dynamics and Control of Process Systems, 2013. IFAC Proceedings series 2013 s. 463-468 http://dx.doi.org/10.3182/20131218-3-IN-2045.00070 © 2013 IFAC.