Hydrocarbon Processing - May 2022 - 44

Digital Technology
richment gas with a high heating value is
added to control the pressure, the heating
value of the fuel gas increases. The furnaces
will then have to cut their consumption,
which will upset the volume balance, and
the enrichment gases will also be cut back.
This behavior keeps repeating, leadFIG.
1. Fuel gas network pressure and quality interaction.
have shifted dramatically during the past
2 yr, with energy reduction, production
cost reduction and increased agility becoming
the top focus areas.
APC and optimization technologies
are the most cost-effective option to improve
process efficiency and support the
decarbonization process. It is worth noting
that with carbon prices continuing to
increase, the value from CO2
reduction
becomes as significant a benefit as energy
reduction savings.
There are a multitude of efficiency
improvement opportunities available to
refiners. These include:
* Increasing furnace efficiency
* Minimizing distillation
columns' pressure against
actual unit constraints
* Minimizing load and
optimizing the purification
level of distillation columns
* Minimizing quality giveaways to
avoid excess energy consumption
* Minimizing circulation,
reprocessing and recycling
* Making the right product recovery/
energy consumption tradeoffs
* Maximizing heat/cold recovery
* Integrating multiple units and
managing heat balances
* Balancing demand/supply to avoid
losses (e.g., hydrogen, steam)
* Minimizing downgrades (e.g.,
steam, fuels, hydrogen, products)
* Maximizing equipment efficiency
* Optimizing utilities networks:
fuels, hydrogen and steam.
APC is now capable of solving difficult
nonlinear problems. In addition, it
enables an adaptive workflow to maximize
model accuracy, lets users make
changes to economic objectives on the
fly, and embeds AI and deep learning capabilities
to leverage a wealth of histori44
MAY 2022 | HydrocarbonProcessing.com
cal data to obtain models and solve complex
problems-for instance, inferring
highly nonlinear qualities that depend
on the type of run, feedstock qualities or
grade to be produced.
With dynamic, self-adapting optimization
capabilities that automatically
align models and the process, a true overall
optimum can be achieved across a
large process scope.
These capabilities and technology
enhancements enable users to solve complex
problems, such as utilities networks,
which were previously difficult to solve.
The rest of this article focuses on optimization
strategies, which
tremendous energy- and CO2
-saving opportunities
in the three primary site networks:
fuel gas, hydrogen and steam.
Fuel gas network optimization. For
many facilities, fuel gas network optimization
can be a significant issue that must
be addressed. The following are some of
the most common challenges:
* Fuel gas network pressure
(volume) and quality
interact heavily
* Volume balance models are
integrating processes as pressure
builds up as a ramp
* Quality models are nonlinear,
and gains may even change from
positive to negative or vice versa
* Various makeup gases are typically
available, and their quality may
change over time
* Additional makeup gas reduces the
effect on overall network quality
due to nonlinearities from mixing
* Inherent instability due to
disturbances continuously
entering the system.
The quality of gas streams entering
the network varies continuously. If enrepresent
ing
to a self-propagating cycle impacting
the performance of fuel gas users across
the whole site. Fuel gas instability can
result in scenarios such as units cutting
back load, increasing load to blowdown
networks and gas recovery compressors,
and flaring events.
This behavior, illustrated in FIG. 1, can
cause excessive energy consumption and
CO2
emissions. With integrating processes
like volume balance models, which
exhibit strong nonlinearity, model gains
can flip their sign (i.e., go from positive
to negative or vice versa) as the quality
effect of the enrichment gas depends on
the current network's calorific value.
Multiple enrichment gases may be
available to control pressure, and each gas
can increase or decrease the network calorific
value. The impact may change over
time depending on current calorific values
of both gas and the network. Adding
more makeup gas changes the effect as the
gap between the two qualities changes.
FIG. 2 illustrates the potential effects
of these nonlinearities. For instance, the
same changes to the same enrichment
gas may have very different effects on the
network's calorific value. Addressing these
challenges is possible by adopting a nonlinear
adaptive process control solutiona
to solve highly nonlinear and interactive
problems and reach desired outcomes.
The adaptive process control solutiona
lets
users easily revise economic objectives to
continuously minimize the overall cost of
the streams entering the fuel gas network.
The following are some other key
benefits of using such a nonlinear adaptive
process control solution:
* Simultaneously control both
fuel gas pressure and quality
(calorific value), stabilizing
operations throughout the site
* Minimize/avoid flaring
* Minimize blowdown discharge
and subsequently gas recovery
compressor power consumption
* Improve furnace efficiency
through consistent and stable
firing, which enables further
reduction in excess air
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