KenanZeynalov/petrol_data · Datasets at Hugging Face

text stringlengths 0 1.75k
reﬁneries
was
facilitated
using
Argonne
National
Laboratory’s Greenhouse gases, Regulated Emissions, and Energy
use in Transportation (GREET™) model [19]. The GHG emissions
calculation combines carbon dioxide, methane and nitrous oxide
with their global warming potentials, which are 1, 25 and 298,
respectively, as recommended by the latest Intergovernmental
Panel on Climate Change for a 100-year time horizon [20].
2. Reﬁnery modeling and analysis approach
In the current study, reﬁnery LP modeling was employed to
simulate and compare the operations of 43 US and 17 EU reﬁneries
with individual processing capacity of over 100,000 bbl/day crude
oil. Note that although the 17 EU reﬁneries account for only 25% of
the total EU reﬁning capacity, their operational characteristics
appear to be quite consistent with aggregate average EU reﬁnery
operations (see Table S1).
The
selected
US
reﬁneries
were
located
in
Petroleum
Administration for Defense Districts (PADDs) 1, 2, 3 and 5, while
the selected EU reﬁneries were located in the coastal regions of
Europe. Reﬁnery LP models typically maximize proﬁt by determin-
ing the optimal volumetric throughput and utility balance among
various process units within a reﬁnery under speciﬁc market and
operation conditions [21]. The output ﬁles from LP model sim-
ulations contain volumetric and mass ﬂow rates associated with
inputs and outputs of process units. Using this information, energy
inputs and outputs can be calculated by using known heating val-
ues of various stream components.
In this study, we grouped the U.S and EU reﬁneries described
above into three different groups according to their average crude
API gravity and HP yield. As shown in Fig. 1, reﬁneries were
J. Han et al. / Fuel 157 (2015) 292–298
293
categorized in the following manner: (1) Low API (API grav-
ity < 29), (2) High API/Low HP (API gravity > 29 and HP < 0.22)
and (3) High API/High HP (API gravity > 29 and HP > 0.22).
Table S2 also shows the operational characteristics of reﬁneries
in each reﬁnery group. Note the almost no overlaps in the key
parameters between the Low API and High API/High HP group.
Among the two High API groups, the Low HP group is clearly more
resource-efﬁcient than the High HP group. It also needs to be noted
that assigning reﬁneries to any of the three reﬁnery groups is not
intended to provide a statistical or physical classiﬁcation among
reﬁneries; rather it is intended to examine the impacts of resource
and energy efﬁciencies on life-cycle GHG emissions. Within each
reﬁnery group, three major metrics were evaluated for each reﬁn-
ery: overall reﬁnery efﬁciency, product-speciﬁc reﬁning efﬁciency
and life-cycle GHG emissions intensity. Based on the volumetric
amounts of reﬁnery inputs and outputs, and purchased electricity
energy
estimated
by
the
LP
modeling,
the
overall
reﬁnery
efﬁciency was estimated by dividing the total energy output by
the total energy input on a lower heating value (LHV) basis
(see Eq. (1)).
where gLHV is the LHV-based overall efﬁciency of a reﬁnery. Pn, Cm
and OIoare the amounts of reﬁning product n (e.g., gasoline, jet fuel,
diesel, liqueﬁed petroleum gas [LPG], RFO, pet coke), crude input
m, and other input material o (e.g., normal butane, iso-butane,
reformate, alkylate and natural gasoline) in barrels for liquid prod-
ucts
and
tons
for
pet
coke,
respectively.
NGpurchased;LHV
and
H2;purchased;LHV are the LHV-based energy of purchased natural gas
(NG) and purchased H2, respectively. Electricitypurchased is the energy
in purchased electricity. LHVm, LHVn, and LHVo are the LHVs of
crude input m, reﬁned product n, and other input material o,
respectively, in MJ/barrel for liquid products and MJ/ton for pet
coke.
In order to calculate the GHG emissions intensity for each
reﬁned product, the product-speciﬁc efﬁciency and process fuel
shares need to be determined. This determination is essential as
each product pool is supplied from a different set of process units,
each with different energy and emissions burdens (see Fig. S2).
Since reﬁnery inputs propagate through individual process units
to ﬁnal products via intermediate products, each intermediate or
ﬁnal product carries with it certain energy and emissions burdens
of the total reﬁnery inputs, such as crude, natural gas, electricity,

reﬁneries

was

facilitated

using

Argonne

National

Laboratory’s Greenhouse gases, Regulated Emissions, and Energy

use in Transportation (GREET™) model [19]. The GHG emissions

calculation combines carbon dioxide, methane and nitrous oxide

with their global warming potentials, which are 1, 25 and 298,

respectively, as recommended by the latest Intergovernmental

Panel on Climate Change for a 100-year time horizon [20].

2. Reﬁnery modeling and analysis approach

In the current study, reﬁnery LP modeling was employed to

simulate and compare the operations of 43 US and 17 EU reﬁneries

with individual processing capacity of over 100,000 bbl/day crude

oil. Note that although the 17 EU reﬁneries account for only 25% of

the total EU reﬁning capacity, their operational characteristics

appear to be quite consistent with aggregate average EU reﬁnery

operations (see Table S1).

The

selected

US

reﬁneries

were

located

in

Petroleum

Administration for Defense Districts (PADDs) 1, 2, 3 and 5, while

the selected EU reﬁneries were located in the coastal regions of

Europe. Reﬁnery LP models typically maximize proﬁt by determin-

ing the optimal volumetric throughput and utility balance among

various process units within a reﬁnery under speciﬁc market and

operation conditions [21]. The output ﬁles from LP model sim-

ulations contain volumetric and mass ﬂow rates associated with

inputs and outputs of process units. Using this information, energy

inputs and outputs can be calculated by using known heating val-

ues of various stream components.

In this study, we grouped the U.S and EU reﬁneries described

above into three different groups according to their average crude

API gravity and HP yield. As shown in Fig. 1, reﬁneries were

J. Han et al. / Fuel 157 (2015) 292–298

293

categorized in the following manner: (1) Low API (API grav-

ity < 29), (2) High API/Low HP (API gravity > 29 and HP < 0.22)

and (3) High API/High HP (API gravity > 29 and HP > 0.22).

Table S2 also shows the operational characteristics of reﬁneries

in each reﬁnery group. Note the almost no overlaps in the key

parameters between the Low API and High API/High HP group.

Among the two High API groups, the Low HP group is clearly more

resource-efﬁcient than the High HP group. It also needs to be noted

that assigning reﬁneries to any of the three reﬁnery groups is not

intended to provide a statistical or physical classiﬁcation among

reﬁneries; rather it is intended to examine the impacts of resource

and energy efﬁciencies on life-cycle GHG emissions. Within each

reﬁnery group, three major metrics were evaluated for each reﬁn-

ery: overall reﬁnery efﬁciency, product-speciﬁc reﬁning efﬁciency

and life-cycle GHG emissions intensity. Based on the volumetric

amounts of reﬁnery inputs and outputs, and purchased electricity

energy

estimated

by

the

LP

modeling,

the

overall

reﬁnery

efﬁciency was estimated by dividing the total energy output by

the total energy input on a lower heating value (LHV) basis

(see Eq. (1)).

where gLHV is the LHV-based overall efﬁciency of a reﬁnery. Pn, Cm

and OIoare the amounts of reﬁning product n (e.g., gasoline, jet fuel,

diesel, liqueﬁed petroleum gas [LPG], RFO, pet coke), crude input

m, and other input material o (e.g., normal butane, iso-butane,

reformate, alkylate and natural gasoline) in barrels for liquid prod-

ucts

and

tons

for

pet

coke,

respectively.

NGpurchased;LHV

and

H2;purchased;LHV are the LHV-based energy of purchased natural gas

(NG) and purchased H2, respectively. Electricitypurchased is the energy

in purchased electricity. LHVm, LHVn, and LHVo are the LHVs of

crude input m, reﬁned product n, and other input material o,

respectively, in MJ/barrel for liquid products and MJ/ton for pet

coke.

In order to calculate the GHG emissions intensity for each

reﬁned product, the product-speciﬁc efﬁciency and process fuel

shares need to be determined. This determination is essential as

each product pool is supplied from a different set of process units,

each with different energy and emissions burdens (see Fig. S2).

Since reﬁnery inputs propagate through individual process units

to ﬁnal products via intermediate products, each intermediate or

ﬁnal product carries with it certain energy and emissions burdens

of the total reﬁnery inputs, such as crude, natural gas, electricity,