Hydrocarbon Processing - May 2022 - 46

Digital Technology
in hydrogen quality and pressure.
* Optimum dynamic operational
changes. Constraints may show
up in different units and scenarios
may change significantly based on
costs and the availability of feeds,
conversion/desulphurization units'
margins and market conditions.
* There are nonlinearities to be
addressed and tradeoffs to be
managed like purities, catalyst
lifecycles, losses and recoveries
based on required hydrogen purities
and associated consumptions.
* There is sometimes a lack of
ownership of coordination of
multiple producers and consumers.
Hydrogen consumption is forecast to
increase in the short- to mid-term due to
multiple reasons, such as tighter desulphurization
targets being enforced and
the need to co-process more renewable
feedstocks in hydrotreaters due to local
regulations. For example, a 3% vegetable
oil coprocessing could require 20%
more hydrogen.
Hydrogen networks interact with
both gasoline and middle distillates'
circuits. Within a gasoline circuit, tradeoffs
may exist between hydrogen production
and gasoline production requirements.
Within a middle distillates
circuit, the tradeoffs are between unit
loads, feed blending (e.g., vegetable oils,
cracked feeds or vacuum gasoils) and
hydrogen availability.
If such tradeoffs are identified, the
most practical way to address them is to
integrate the hydrogen network optimizer
with the production circuit optimizer(s).
With a scope encompassing production
circuits (targeting reduction of quality
giveaways), additional benefits in
terms of energy and CO2
savings can be
achieved, as any giveaway results in additional
energy consumption.
A hydrogen network can be effectively
optimized by leveraging dynamic optimization
technologyc
in combination with
an adaptive process control solution.a
This addresses nonlinearities and coordinates
multiple units dynamically by relying
on an ever-green adaptive APC layer
to reach desired objectives.
Such an application typically enables
users to:
* Balance production of and
demand for hydrogen, ensuring
it is optimally distributed
* Optimize steam reformer
operations and conversion
* Minimize hydrogen discharge to
fuel gas network and blowdown
* Reduce power consumption in gas
recovery compressor(s), if any
* Minimize hydrogen
production costs whenever this
flexibility exists
* Increase profitability
of conversion and
desulfurization units
* Stabilize hydrogen header
pressures and purities
* Optimize hydrogen
network purities
* Significantly reduce
CO2
emissions.
Assuming the 200,000-bpd European
FIG. 5. Approximately 1 tph of hydrogen discharged to fuel gas over 2 mos for a
200,000-bpd refinery.
refinery discharges 500 kg/hr of hydrogen
to fuel gas, it can reduce the loss by
at least 50%, reduce CO2
18,000 tpy and achieve savings of approximately
$4 MM/yr. Additional benefits
can be realized due to reduced hydrogen
production costs and more profits coming
from hydrogen users.
It is not uncommon for sites to discharge
approximately 1 t/hr of hydrogen
or more on average to the fuel gas network,
as shown in FIG. 5.
The following are some typical available
control handles:
* Steam reformer(s)
hydrogen production
* Reformer(s) severities
(when possible and not fixed
on gasoline balance)
FIG. 6. Hydrogen discharged to the flare 2 yr before and after the dynamic optimization
technologyc
" Dynamic optimization moves into mainstream, " ERTC, November 2018.
46 MAY 2022 | HydrocarbonProcessing.com
* Bypasses between headers
of different purities
was implemented in a 220,000-bpd refinery. Source: A. Porcel and K. Kahlgren,
* Purge rates from
consuming units
emissions by
http://www.HydrocarbonProcessing.com
Hydrocarbon Processing - May 2022

Table of Contents for the Digital Edition of Hydrocarbon Processing - May 2022
