Chemical Engineering September 2020 - 38

Optimization
results
Pareto front
does not rely on individual sensors'
threshold values. Instead, multiple
sensors are combined together,
pump key performance indicators
are evaluated and compared to their
expected value. In several cases,
this approach can provide an indication
of the developing issue much
earlier than the traditional thresholdmonitoring
approach. The latter
usually triggers an alert only seconds
before the actual failure.
Distance from throughput target, %
Figure 9. Shown here is a pareto front between
two competing targets of a pipeline system
the number of catastrophic failures,
which have a significant impact on
repair costs and downtimes. To
achieve this reliability goal, machinelearning
techniques are employed.
More precisely, a combination of
unsupervised
anomaly
detection
with pump-physics-driven modeling
is selected. While the specificity of
anomaly detection techniques can
be inadequate, the problem space
may be reduced considerably by
imposing constraints on variables,
especially by modeling the correlations
between components [4].
The target is to determine if a
pump is operating according to
normal operation standards or abnormality
evidence is indicating an
imminent failure. For that reason,
the training data are used to learn a
model of the normal behavior. When
doing inference, new data are compared
to the expectation and then
classified. A review of common such
methods can be found in Ref. 5. As
seen in Figure 7, after every major
maintenance action of the pump,
a model representing the healthy
asset is trained, which is used as
the benchmark model for any future
operation. Such models shall represent
the pump healthy operation
at the full range of operating conditions.
For every new timestamp
available, the pump conditions are
compared to the healthy benchmark
model and every deviation and abnormality
is a potential indication of
a developing issue.
The key point of the proposed
solution is that such a method
38
Optimization capabilities
By establishing performance and
reliability predictions, the next target
of this solution is to offer optimization
capabilities to the user. An
optimizer shall trigger simulations
of the pump system and based
on an optimization strategy and
boundary conditions, the operation
can be optimized for single or
multiple targets. The pump system
performance can be optimized, by
ensuring the minimum energy is
consumed for given load requirements.
Another target can be the
operation shift close to preferred
conditions, achieving also maximization
of the reliability index.
Furthermore, the use of drag-reducing
agents can be optimized,
by ensuring at the same time that
the pump system will operate at
the desired conditions. It is apparent
that all discussed optimization
strategies also have a positive impact
on the financial targets of the
system operation.
An optimization example is
shown in Figure 8, where competing
targets are illustrated. A typical
target of a pipeline operator is
throughput maximization. In addition,
any reduction in operational
costs (that is, electricity or fuel
consumption) is of great interest.
Last but not least, ideally all
pumps shall operate as close as
possible to their BEP, to ensure
performance and reliability benefits.
Based on the established
data reconciliation method, physical
pump and pipeline models are
adapted to represent the current
equipment behavior.
In the proposed approach, an
optimization method shall set the
boundary conditions and trigger
simulations of the pump system
digital twin. The optimizer shall
evaluate the simulation results and
classify them according to the predefined
target or targets. Several
simulation
optimization
methods
are available in the literature (for
instance, gradient-based search,
heuristic methods and so on), a
critical review of available methods
is presented in Ref. 6.
An example of the expected optimization
results is shown in Figure
9. In case of two competing targets
(that is, maximum system throughput
and pump operation at BEP), a
number of optimal solutions formulate
a pareto front. The algorithm offers
a number of viable solutions to
the operator to choose from. ■
Edited by Gerald Ondrey
Note
This article was first presented at the
VDMA's 4th International Rotating
Equipment Conference, Wiesbaden,
Germany, September 24-25, 2019.
References
1. Romagnoli, J.A. and Sanchez M.C., " Data Processing and Reconciliation
for Chemical Process Operations, " Academic Press,
California, 2000.
2. Gülich, J.F., " Centrifugal Pumps, " 2nd ed., Springer, Heidelberg,
Germany, 2010.
3. Larock, B.E., Jeppson, R.W. and Watters, G.Z., " Hydraulics of
Pipeline Systems, " CRC Press, Florida, 1999.
4. Svendsen, N., Wolthusen, S., Using Physical Models for Anomaly
Detection in Control Systems. in: Palmer, C., Shenoi, S., Critical
Infrastructure Protection III. IFIP Advances in Information and
Communication Technology, vol. 311. Springer, Berlin, Heidelberg,
2009.
5. Pimentel, M.A.F, Clifton, D.A., Clifton, L. and Tarassenko, L., A
Review of Novelty Detection, Signal Processing, vol. 99, pp.
215-249, 2014.
6. Carson, Y. and Maria, A., Simulation Optimization: Methods and
Applications, Proceedings of the 1997 Winter Simulation Conference,
Atlanta, Ga., 1, pp. 118-126, 1998.
Author
Stylianos Giannoulakis is a data
scientist and machine diagnostics
engineer in the Global Technology
division of Sulzer Pumps Equipment
(Neuwiesenstrasse 15,
8401 Winterthur, Switzerland;
Email:
stylianos.giannoulakis@
sulzer.com). He received a Diploma
in Mechanical Engineering
from National Technical University
of Athens (NTUA) and an M.Sc. in Energy Science and
Technology from Swiss Federal Institute of Technology
(ETH Zurich). He leads data analytics, machine learning
and software development prototyping for pumps
equipment. He supervises the complete lifecycle of
data analytics activities, from research/prototyping to
operationalizing new product developments as customer
products offerings.
ChemiCal engineering www.Chemengonline.Com September 2020
Distance from BEP, %
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