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Allocation of energy inputs among the end-uses in the US petroleum and coal products industry
ARTICLE IN PRESS
Energy 32 (2007) 1460–1470 www.elsevier.com/locate/energy
Allocation of energy inputs among the end-uses in the US petroleum and coal products industry Nesrin Ozalp,1, Barry Hyman Mechanical Engineering Department, University of Washington, Box 352600, Seattle, WA 98195, USA Received 13 September 2005
Abstract This paper models the allocation of energy inputs in the US petroleum and coal products industry by allocating combustible fuel and renewable energy inputs among generic end-uses, including intermediate conversions through onsite power and steam generation. This analysis, called an energy end-use model, showed that 72% of the fuel input in the US petroleum and coal products industry goes to onsite steam and power generation, whereas 28% goes directly to end-uses. Eight percent of the boiler output is used for power generation, 72% goes directly to end-uses, and 20% is waste heat. Among the end-uses, process heating is the biggest energy user with a total energy consumption of 2338 PJ, whereas machine drive is the biggest electricity consumer with a consumption of 168 PJ. This paper also provides estimates of the uncertainty of the data. The approach to create this model is applicable to all other industries for which data is available and the model is consistent with US Department of Energy data for 1998. When used in conjunction with similar models for other years, it can be used to identify the changes and trends in energy utilization even at the prime mover level of detail. r 2006 Elsevier Ltd. All rights reserved. Keywords: Energy; Allocation; End-use; Petroleum industry; United states
1. Introduction The US petroleum and coal products industry is the largest refined petroleum products producer in the world and the largest energy consuming industry in the US [1]. The petroleum refining subsector of the US petroleum and coal products industry accounts for 96% of the industry’s energy consumption in 1998 [2]. The objective of this paper is to provide a detailed energy end-use model of the US petroleum and coal products industry. Such a model provides energy flows for onsite power and steam, and end-uses such as heating ventilating and air conditioning (HVAC), machine drive, lighting, etc. An energy end-use model can help identify opportunities to improve energy efficiencies and enhance energy policies [3]. It can also serve as key for other studies such as energy Corresponding author. Tel.: +90 546 439 8880; fax: +90 232 388 8562.
E-mail address: [email protected] (N. Ozalp). Current address: Ege University, Mechanical Engineering Department, 35100 Bornova, Izmir, Turkey. 1
0360-5442/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2006.09.006
process-step models [4–6], energy cost and exergy analysis for manufacturing industries [7–9]. There have been a number of prior efforts to study energy end-uses in manufacturing industries such as: paper industry end-use models using 1991 data [10] and 1998 data [11]; a steel industry combined end-use and process-step model for 1994 [12]; energy end-use models for several manufacturing industries for 1994 [13] and 1998 [14]. In this paper, we follow the same modeling approach used in [11]. However, in this paper, we go beyond that work by assessing the accuracy of the data, and employing a more sophisticated approach to allocating steam and waste heat to end-uses.
2. Databases The primary data source used to construct energy enduse models is the manufacturing energy consumption survey (MECS) issued by the Energy Information Administration (EIA) of the US Department of Energy. The
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secondary data that was used is EIA 860B: Annual electric generator report for non-utility. A brief description of these and other databases are given in this section.
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manufacturing sector. See [26] for additional details on the MECS survey design and implementation. 3. Methodology
2.1. Manufacturing energy consumption survey (MECS) MECS [15] includes data on fuel inputs for heat, power and electricity generation and allocation of inputs to generic end-uses. It also contains data on electricity and steam purchases/sales. The most recent complete MECS data available during the time of this study was 1998. The complete MECS data for 2002 was released in June 2005. MECS uses the North American industrial classification system (NAICS) in order to classify each industry. The NAICS code for the petroleum and coal products industry is NAICS 324. Although MECS provides comprehensive good quality data, as others have noted ‘‘y there are several issues and discrepancies with the MECS data.’’ [13]. In particular, the major gaps in the MECS data are: 1. steam use by component and application is not reported. As an example, a steam turbine is a component, and an example application is electricity generation; 2. steam allocation to end-uses such as process heating is not reported; 3. recovered waste heat is not reported; 4. recovered waste heat allocation to end-uses is not reported; 5. on-site electricity generation by component is not reported; 6. allocation of ‘‘other fuels’’ among components or applications or end-uses is not reported; 7. some data is withheld to protect proprietary information.
Constructing an energy end-use model includes using MECS data to build an energy utilization table (Table 1) and an end-use table (Table 2). These tables show the 95% confidence intervals for the data values based on relative standards of error (RSE) tabulated in MECS. Numerical values less than 0.5 PJ are indicated in Tables 1 and 2 by asterisks. Values denoted by Q represent data withheld by MECS because RSE450%. The details of how to use these tables to assemble an energy end-use model is provided elsewhere [11] and summarized below. 3.1. Filling in the missing values in the tables We begin filling in the missing parts in Tables 1 and 2 by converting asterisks to zero. Then, we can fill in the Q values based on column summation. For example, the value for residual fuel oil used in machine drive in Table 2 should be 2 PJ to make the total direct process uses column summation 33 PJ. Note that some of the columns are unbalanced by about 1 PJ due to round off errors. Table 1 Inputs for heat, power and electricity generation in petroleum and coal products industry, 1998, PJ MECS Source
Energy form
Amount
Table N3.2
Total Net electricity Residual fuel oil Distillate fuel oil Natural gas LPG and NGL Coal Coke and breeze Other
38217157.81 13375.49 7674.93 2472.12 1061756.34 4173.63 * 0 24857175.94
Table N5.1
Total byproducts Blast furnace/coke oven gases Waste gas Petroleum coke Pulping liquor or black liquor Wood chips, bark Wastea
21457131.06 270.05 1476748.56 666731.30 0 0 170.07
Table N13.1
Net demand for electricity Purchases Transfers in Total onsite generation Sales and/or transfers offsite
19574.84 13978.19 * 6171.51 670.15
2.3. Other data sources
Table N13.2
There are various public and private data sources providing information on energy use in the manufacturing sector [18–25] but none of them provide data that can be combined with the MECS and 860B to give a more complete picture of national energy end-use patterns in the
Total onsite generation Cogeneration Renewable energyb Other
6173.48 5872.65 0 470.21
Table N11.3
Purchased steam
9873.15
2.2. Energy information administration’s 860B data (EIA860B): Annual electric generator report for non-utility EIA 860B [16] includes data for all non-utility plants, based on their NAICS codes, whose capacity is at least 1 MW. We used this data for the onsite power and steam generation part of the model. It allows us to calculate electric conversion and waste heat recovery efficiencies at the prime mover level based on actual operating data. The details of these calculations are provided elsewhere [17].
a
Waste including waste oils, tars and waste materials. Renewable energy excluding wood and other biomass.
b
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Table 2 End-use data for NAICS 324 extracted from MECS Tables N6.2 and N6.4, 1998, PJ MECS end-uses
Net electricity
Residual fuel oil
Distillate oil and diesel fuel
Natural gas
LPG
Coala
Other
Net demand for electricity
Total fuel consumption
13376.8
7677.21
2471.23
1061761.96
4174.19
*
24857NA
195710.76
Indirect uses (Boiler fuel)
170.07
4375.31
370.2
276720.98
770.93
*
—
170.07
Direct uses (Total process uses) Process heating Process cooling and refrigeration Machine drive Electro-chemical processes Other process use Direct uses (Total non-process use) Facility HVAC Facility lighting Facility support Onsite transportation Conventional electricity gen. Other non-process use
12275.55 27NA 570.25 11577.81 * 0 1170.5 470.28 470.03 170.02 * — *
3372.79 317NA 0 Q — 0 0 0 — 0 — 0 0
1570.68 137NA 0 270.14 — 0 570.23 * — * 470.18 * *
705736.66 6887NA 270.11 1371.01 — 170.08 8074.16 870.63 — 670.12 0 6471.43 170.01
3373 277NA 270.2 Q — 0 170.09 * — * * * 0
0 0 0 0 — 0 0 0 — 0 — 0 0
— — — — — — — — — — — — —
17878.54 271.10 870.37 168711.69 * 0 1570.85 770.57 570.05 270.04 * — *
End-use not reported
*
0
Q
*
*
0
24857NA
*
a
Coal excluding coal coke and breeze.
3.2. Fuel and net steam inputs in the energy end-use model
3.6. Allocation of fuels and electricity to end-uses
The fuel inputs from Table 2 are displayed in the lower left corner of Fig. 1. Following the procedure described in detail in [11] and assuming that steam from noncombustible renewables is zero, we conclude that other energy sources except net steam ¼ 2387 PJ and net steam ¼ 98 PJ (displayed in the middle left side of Fig. 1). Note that net steam is not part of onsite steam generation.
End-uses are located on the right side of Fig. 1. The values in Table 2 were used to allocate 808 PJ of commercial fuels and 193 PJ of electricity to these enduses. However, Table 2 does not allocate ‘‘Other’’ fuels to either onsite power and steam generation or generic enduses. But the onsite conversion model based on EIA 860B data and shown in Fig. 2 requires 2590 PJ total fuel input to boilers and prime movers. A backward calculation indicates that 2196 PJ of ‘‘Other’’ should go to for onsite power and steam generation, with the remaining 191 PJ delivered directly to end-uses. Combined with the commercial fuels, that leads to 999 PJ of fuel allocated to end uses. The distribution of commercial fuels among the end uses is provided in Table 2. We assume that the 191 PJ of ‘‘Other’’ fuels is allocated among the end-uses in the same proportion as the commercial fuels. See Fig. 3 for an explicit allocation of fuels to these end-uses.
3.3. Offsite electricity The acquisition and disposition of electricity is presented in the upper left corner of Fig. 1 as purchased electricity, electricity sold, and electricity from noncombustible renewables. These values are taken from Table 1. 3.4. Onsite power and steam generation A condensed version of our onsite power and steam generation model for the petroleum industry is shown in the upper middle portion of Fig. 1, and includes 61 PJ of onsite electricity, 1784 PJ of boiler steam for end-uses, and 156 PJ of recovered waste heat. The detailed model, which was created elsewhere [17] from EIA 860B 1998 data is shown in Fig. 2. 3.5. Steam loss We assumed an average 30% energy loss during steam distribution due to heat transfer, ineffective steam traps, leaks etc. as was done in [11].
3.7. Allocation of recovered waste heat to end-uses In contrast to commercial fuels and electricity allocations to end-uses, there is no MECS data for allocating recovered waste heat to end-uses. However, EIA 860B provides data on the amount of waste heat recovered and provides some descriptive information on the applications of recovered waste heat. This information is summarized in the Table 3 column ‘‘Original data’’. When there are multiple applications for waste heat in a given facility (see the last five rows of Table 3), 860B identifies the multiple applications but does not quantify the allocations among those applications. To resolve this ambiguity, we assume
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Purchased Electricity
1463
194
55 6
Electricity Sold
Unrecovered Waste Heat 61
Electricity from Noncombustible Renewables
0
586
1
257
Onsite Electricity Generation
156 Waste Heat
62 380 491
2261
2 Process Heating
185
195
Boiler
2395
5 1784
98 Net Steam
Process Cooling and Refrigeration
8
2590 Distribution Losses 43 76
168
64 Machine Drive
Export 565
3
33
0
Electro-Chemical Processes
107
Other Process Use
Residual Fuel Oil 153
1317
0
3 Distillate Fuel Oil
21
24
341 Natural Gas
720
1061
999 Liquified Petroleum Gas
41
0
0
2316
2469
193 17
7 Facility HVAC
7
34 0
0
0
5 Facility Lighting
Coal 0
0
72
0
Facility Support
Coke and Breeze Other Energy Sources Except Net Steam
2387
2196
191
50
Onsite Transportation
10
Other NonProcess Use
Fig. 1. Energy end-use model of NAICS 324 in 1998, PJ.
that process steam accounts for 60% of recovered waste heat when there are dual applications, and 50% of recovered waste heat when there are three or more applications; with the remainder divided equally among the non-process heat applications. This allows us to
subdivide the multiple applications and adjust the first five rows at the top of Table 3 to obtain reassigned allocations. Note that the 860B term ‘‘application’’ used in Table 3 is not the same as the MECS term ‘‘end-use’’. In particular, we interpret the process steam application values in Table 3
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1464
Non combustible renewables
0
Electricity generated Unrecovered waste heat
63
<1
1
46
87
Steam turbine
Gas turbine
ICE
586
17 168
Electricity input
Waste heat 491
156
Recovered waste heat to end-uses
1
194
1
62
185 2395
Boiler
2590
1784
Fuel input
Steam to end-uses
Fig. 2. On-site power and steam generation model of NAICS 324 in 1998, PJ.
to be recovered waste heat in the form of steam. To test the reasonableness of this approach, we use the results from [17] for the amount of waste heat recovered from on-site power generation in steam turbines and combined cycle plants, and assume that all of that recovered waste heat is in the form of steam. The steam turbine with heat recovery is modeled in Fig. 4, taken from Fig. 3 in [17]; the combined cycle is modeled in Fig. 5, taken from Fig. 4 in [17]. From the numerical values listed in Tables 3 and 6 in [17], we obtain
temperature process heating activity. If credible data on the amount of this energy transfer were available, we could divide the process heating end-use box in Fig. 3 into hightemperature process heating and low-temperature process heating boxes and show the energy flow between them. Another example of recovered waste heat not included in our analysis is the use of waste heat from process heating end-use for use in another end-use such as facility HVAC. Again, if data were available, this energy transfer could be depicted by an arrow connecting the two end-uses in Fig. 3.
Waste heat recovered as steam
3.8. Allocation of steam to end-uses
¼ x14 þ x25 ¼ 71 þ 58 ¼ 129 PJ. So the reassigned process steam allocation value in Table 3 is within 13% of the value in [17] for waste heat recovered as steam from onsite cogeneration. Hence, we will add the 112 PJ of reassigned process steam in Table 3 to the 1317 PJ of net steam and boiler output (after distribution losses) and allocate it to end-uses as described in the next section. Since the waste heat recovered as steam is described in 860B as ‘‘net useful thermal energy’’, we will not apply the steam distribution loss factor to this amount. The reassigned value of 3 PJ waste heat in Table 3 is depicted as Exports in Fig. 1. The remaining reassigned allocations of waste heat are associated with the end-uses listed in the last column of Table 3, and displayed explicitly in Fig. 3. The recovered waste heat discussed in this section consists of waste heat recovered from fuel fired boilers and onsite power generators. Not included in this analysis is waste heat that may be recovered from an end-use. For example, waste heat from a high-temperature process heating activity may be recovered for use in a low-
In this section, we allocate the 1317 PJ of net steam and boiler steam (see middle bottom of Fig. 1) and the 112 PJ of waste heat recovered as steam (from Table 3) to the enduses. Here we are faced with a challenge similar to the one described in the previous section—there is no MECS data on allocation of steam to end-uses. Allocating steam to end-uses is even more difficult because, unlike the case with recovered waste heat, 860B provides no insight into the allocation of boiler steam or net steam. Hence, we turn to process-step models of petroleum refining for insights into end-uses of steam. However, process-step models of industrial processes inherently involve many assumptions about operating parameters such as temperatures, pressures, etc. for each step in a multi-step manufacturing process. In addition, multiple processes are frequently available to produce a given product. Many of these process-step details involve proprietary information, and we are unaware of any statistical-based representative quantitative model of petroleum refining in the open literature. In fact, one of the major values of an end-use model of petroleum refining based on MECS and 860B data is to use it to calibrate
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939 1310 12
Process Heating
5 0 0
Process Cooling and Refrigeration
26 38 0
Machine Drive
168
0 0 0
Electro-Chemical Processes
0
1 82 24
Other Process Uses
1465
Table 3 EIA 860B data for recovered waste heat in the petroleum industry
2
860B applications
8
Original data
Reassigned allocations
MECS end-use
see Section 3.8 of text Process heating Facility HVAC
Process steam (PS)
95
112
Direct heat (DH) Space heat (SH) Other end users (EU) Other (OT) PS+DH PS+SH PS+EU PS+SH +DH PS+SH+OT+EU
8 0 0 23 5 5 5 8 8
12 5 3 24 — — — — —
156
156
Total
Other process uses
999 unrecovered waste heat
0
1429
193 12 0 5
x11
Facility HVAC
7 steam turbine 5
41 0 0 0
x15
Facility Lighting
waste heat 7
x13
5
x14
to end-uses
x17 x12
7 0 0
Facility Support
5 0 0
Onsite Transportation
boiler 6
2 x16
Fig. 4. Model of power generation and waste heat recovery from steam turbine.
0
unrecovered waste heat Other NonProcess Uses
x18
0
1 0 0
fuel steam recovered waste heat electricity Fig. 3. Allocation of fuel, steam, waste heat and electricity among end-uses in NAICS 324 in 1998, PJ.
gas turbine 8
x21 steam turbine 9
x26
x23
x19
waste heat 11
x25
to end-uses
x24 x22 x20
x27
boiler 10
Fig. 5. Model of power generation and waste heat recovery from combined cycle.
a process-step model of petroleum refining to make the process-step model consistent with national data. Nevertheless, in the absence of national data on steam allocation to end-uses in this industry, we turn to processstep models of petroleum refining to gain at least some insight into end-uses of steam.
3.8.1. Steam usage in refining processes The most detailed quantitative process-step model of petroleum refining was developed in the late 1970s at Drexel University [27]. The Drexel model provides mass
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about the same time as [27]. The estimates presented in [30] are in terms of unit input to each process; we combined them with the input values from [29] to get the values shown in Table 4. Steam consumption patterns from two more-contemporary analyses of energy use in petroleum refining [31,32] are also shown in Table 4. None of these studies [29–32] contain the detailed mass and energy balances at each process-step that are included in [27] so they are not true process-step models in the sense that [27] is. Another contemporary study [33] does not explicitly identify steam consumption for many process steps, instead indicating qualitatively that energy consumption for some processes include purchased steam and expressing energy consumption in terms of the fuels used to produce steam onsite. Since most of these fuels are also consumed directly in end-uses (see energy flows from left to right at the bottom of Fig. 1) there is no way to distinguish in [33] between the direct use of these fuels and the amount of steam generated by these fuels. However, [33] does explicitly quantify (on a national basis for 1996) steam consumption for fluid coking, and flexicoking; it also quantifies steam production from fluid coking, flexicoking, fluid catalytic cracking, catalytic hydrocracking, catalytic reforming, and catalytic hydrotreating. These processes are listed in Tables 4, but since total steam consumption is not known, we cannot use [33] to provide a distribution of steam consumption among all process steps.
and energy balances at each process step. Although much of the quantitative estimates are based on anecdotal information, and the model has never been updated to reflect new data and changes in process design and technologies, it continues to be used as the reference process-step model for petroleum refining [28]. The petroleum refining process steps identified in [27] that consume steam and the percentage of total steam consumed in each step are listed in Table 4 (we use percentage of steam consumption because it is less likely to change from year-to-year than the amount of steam consumed). The Drexel model also identifies processes that produce steam. The end-use model in Fig. 1 only accounts for purchased steam, steam produced in a boiler from fuel for power generation or direct distribution to end-uses, and steam recovered from onsite power generation via a steam turbine or combined cycle. Steam produced in one industrial process and used in another industrial process is not depicted in Fig. 1. This is similar to the situation discussed in the previous section with respect to recovered waste heat, in that steam generated in one end-use for use in another end-use could be depicted in a more detailed version of Fig. 3 if credible data were available. In the absence of such data, we choose not to include steam production from industrial processes in our end-use model. Several other estimates of steam use in petroleum refining [29,30] also shown in Table 4 were published
Table 4 Comparison of steam consumption patterns in petroleum refining processes (%) Process category
Process
Drexel [27]
Chiogioji [29]
EEA [30]
Resource dynamics [31]
LBL [32]
Atmospheric distillation Vacuum distillation Thermal cracking Visbreaking Lube oil processing Asphalt processing Fluid catalytic cracking
9 10
16 13 4 1 p.n.i. p.n.i.
35 32
27 14
24 13
o1* p.n.i p.n.i 0
0 0 0
15 1
1*
Coking Delayed coking Fluid coking Flexicoking Catalytic hydrocracking Catalytic hydrotreating Catalytic reforming Distillate hydroforming Hydrogen treating Alkylation Isomerization
53 p.n.i. p.n.i.
5 1 p.n.i. 1 16
3 2 2
5
p.n.i. p.n.i. p.n.i.
30
2
4 23 13 p.n.i. p.n.i 15
2 1
1 n.e.i.
1 3
25 0
1 Isobutane Isopentane/isohexane Gas recovery Other Total
p.n.i. process not included in model. n.e.i. amount of steam not explicitly identified. * not included in total; see text.
4 27 10 p.n.i 0 12 4
5
10
100
100
p.n.i. 100
100
0 6 100
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Table 4 indicates that [31,32] are in very close agreement on steam consumption patterns, and provide a more contemporary picture of steam consumption than either [27,29,30]. Therefore, we will assume that the total steam utilization for petroleum refining in 1998 was distributed among refining processes according to the pattern described in [31]. Though [31] does show a small amount of net steam production for coking operations, we combined the value in [31] with data in [33] to show that the steam input to coking operations accounts for less than 1% of all steam consumption reported in [31]. We therefore, neglect steam input for coking operations, and use the same logic to neglect a similarly small (but unquantifiable) steam input for visbreaking. 3.8.2. Assignment of process steps to end-uses The next step in allocating steam to end-uses is to map the process steps listed in Table 4 to the MECS end-uses. Then the steam distribution among process steps, as determined in Table 4, can be assigned to end-uses. Unfortunately, this mapping is complicated by the fact that many petroleum refining processes involve multiple end-uses. We begin with Table 5 that matches the MECS end-uses with the purposes for which steam is used in petroleum refining as described in [31]. Thus, it is clear from Table 5 that steam use in petroleum refining should be allocated to four MECS end uses: process heat, process cooling and refrigeration, machine drive, and other process uses. The remaining task is to quantify this allocation. Many of the steam consuming processes listed in Table 4 (plus non-steam consuming processes not included in Table 4) require machine drive to operate those processes. However, none of the studies described in [27,29–33] quantify these machine drive requirements, much less indicate how much of that machine drive is in the form of electric motors, diesel engines, gas turbines, or steam turbines. The only information we have been able to identify regarding steam for machine drive in the petroleum industry is that ‘‘steam turbines account for a large amount of energy used in machine-drive applications.’’ (p. 54 in [31]). While MECS provides data on electricity and fuel for
Table 5 Correspondence between purposes for steam use [31] and MECS end-uses Purposes for steam use [31]
Corresponding MECS end-use
Stripping Fractionation Power generation
Other process use Other process use Onsite electricity generation—but this is not an enduse; it is an intermediate use accounted for elsewhere in the model (see upper left part of Fig. 1). Machine drive Process cooling and refrigeration Other process use Process heating Other process use
Mechanical drive Quenching Dilution Process heating Vacuum draw
1467
machine drive, it does not contain data on steam use for machine drive. Hence, we will assume that machine-drive accounts for the same percent of all steam consumed in enduses as it does for fuel used for process end-uses. Based on Fig. 3, we therefore conclude that machine-drive accounts for 26=ð939 þ 5 þ 26 þ 1Þ ¼ 2:7% of steam consumed in end-uses. This amounts to 38 PJ. The remaining 97.3% (1391 PJ) is divided among process heat, process cooling and refrigeration, and other process uses. Quantitative estimates for allocating this 1391 PJ of steam to these end-uses were provided by [34]. Combining those estimates with the information from [31] in Table 4 leads to the two-step assignments displayed in Fig. 6. The results for steam consumption in end-uses also are shown in Fig. 3, and contribute to the condensed version of Fig. 3 shown in the lower right corner of Fig. 1. 4. Results and discussion This paper presents an energy end-use model for the petroleum and coal products industry (NAICS 324) based on 1998 MECS and EIA 860B data. The model improves in several respects on other end-use models conducted for this and other industries [10–14]. 4.1. Allocation of inputs to end-uses As shown in Fig. 1, 72% of the fuel input goes to onsite steam and power generation whereas 28% goes directly to end-uses. Approximately 92% of the fuel for onsite steam and power goes through the boiler and 8% goes directly to onsite electricity generation. Seventy-two percent of the boiler output goes directly to end-uses, whereas 20% goes to waste heat and the rest to onsite electricity generation. We also see that the contribution of the boiler to the enduses is 18 times greater than that of net steam. Although 1882 PJ goes to end-uses from the boiler and net steam combined, 30% of this amount is lost due to distribution. There is no renewable energy utilization for on-site electricity generation. Onsite electricity accounts for 28% of the total electricity to end-uses, with purchased electricity contributing the remainder. Six PJ of electricity generated onsite is sent to the grid. We see that 2469 PJ is supplied to end-uses by fuel, steam, and recovered waste heat while only 193 PJ is supplied to end-uses by electricity. Process heating is the biggest energy consuming end-use, followed by machine drive. Energy input to the other enduses is considerably smaller. As seen in Fig. 3, the dominant contributors to process heating are fuel (41%) and steam (58%). Electricity accounts for 72% of the input to machine drive, with fuel and steam contributing 11% and 16%, respectively. Machine drive consumes 87% of the electricity furnished to end-uses, with the remainder scattered over five other end-uses.
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Processes
End-Uses
Atmospheric Distillation 37 6 Vacuum Distillation 188 376
1305 Process Heat
1
Fluid Catalytic Cracking
46 188 Catalytic Hydrocracking
324
1 51
Catalytic Hydrotreating
5
161
324
1429
Steam
179
Catalytic Reforming
Other
38
Machine Drive
213
213 59
82 18
59 Alkylation
Isomerization
Fig. 6. Allocation of steam among processes and end uses in NAICS 324 in 1998, PJ.
4.2. Accuracy of the model The fuel and electricity inputs and their allocations to end-uses are based on 1998 MECS data. As displayed in Tables 1 and 2, this data is of high quality. With few exceptions, the 95% confidence intervals are less than 10% of the nominal values. Of even higher quality is the portion of the model that depicts onsite electricity and steam generation, including waste heat recovery, which is based on EIA 860B data for all operating onsite power plants 41 MW in NAICS 324 [17]. Several key assumptions, namely those for steam boiler efficiency (80%) and steam distribution losses (30%), are reasonable estimates based on values appearing in the literature. As shown in Table 3, EIA 860B provides unambiguous allocation of 81% of the recovered waste heat from onsite power and steam production to process steam and to three MECS end-uses. We made reasonable allocations for the remaining 19% in a manner that the reassigned recovered waste heat in the form of process steam was within 13% of the 860B data for recovered waste heat from steam turbines and combined cycle plants. The allocation of steam (from boilers, net steam, and recovered waste heat in the form of process steam) to enduses required a two-phase assignment process depicted in Fig. 6. The first phase involved distributing steam among refinery processes using the estimates in [31]. The second phase involved mapping the steam use in each process to one or more of the generic end-uses. Lacking any published information on this mapping, we relied on expert judgment [34]. In addition, we assumed that the proportion of steam
used for machine drive was equal to the proportion of fuel used for machine drive. 4.3. Comparison of the results with other energy end-use models Table 6 compares our energy end-use model with two other energy end-use models for the petroleum industry [13,14]. This comparison shows that the fuel consumption presented in this study is 1% higher than that given in [14], and 9% higher than that given in [13]. The reason for the difference between the fuel consumption reported in this study and [14] is because we modeled NAICS 324 while [14] models the NAICS 324110 subsector of NAICS 324. The reason for the difference between this study and [13] is because we used 1998 MECS data and [13] used 1994 MECS data. While the steam production estimate provided in this study is reasonably close to that in [13], it is 2.3 times more than the estimates provided in [14]. This difference can be attributed to the assumption made in [14] that most of the ‘‘Other’’ energy sources are used directly for end-uses, while we had over 90% of ‘‘Other’’ going to onsite steam and power generation to satisfy the fuel input requirements of the onsite steam and power production model. Since the onsite steam and power generation model is based on actual operating data contained in 860B, our results are deemed to a better representation of actual steam production than [14]. For the same reason, our estimate for the process heating end-use is somewhat less than that in both [13,14]. The major difference in the distribution
ARTICLE IN PRESS N. Ozalp, B. Hyman / Energy 32 (2007) 1460–1470 Table 6 Comparison of fuels, steam, electricity and losses, PJ This study, 1998
Energy footprints [14], 1998
Fuels Residual fuel oil 76 Distillate fuel oil 24 Natural gas 1061 Liquefied petroleum gas 41 Coal 0 Coke and breeze 0 Other energy sources 2387 Conventional fuel — purchase Other fuels — Total fuel purchase/ 3589 consumption Steam Steam total Electricity Onsite electricity generation Electricity from renewables Electricity sold Electricity purchase Cogenerated electricity Conventional electricity End-uses Process heating Process cooling Machine drive Electro-chemical processes Other process use End-uses HVAC Lighting Facility support Onsite transportation Other non-process use Losses Unrecovered heat Distribution losses
Little [13], 1994
— — — — — — — —
— — — — — — — 1012
— 3540
2257 3269
2067
896
2252
61
55
53
0
—
0
6 139 — —
— 130 — —
7 135 52 1
2261 13 232 0
2898 22 188 —
2804 16 268 0
107
—
30
24 5 9 5 1 586 565
9 > = > ;
8 > <
3 —
14 5 2 2 1
— 480
— —
53
> :
losses (primarily steam) between this study and [14] are also the result of this study estimating more than twice as much steam production as [14], plus [14] assuming a 20% steam distribution loss compared to our 30%. The electricity generation for all three studies are in close agreement, with the differences again attributable to the difference in NAICS for [14] and year for [13]. In terms of end-uses, all three studies show process heating being the dominant end-use by far, accounting for between 85% (this study) and 92% [14] of energy delivered to end-uses. All three studies also agree that machine drive is the next largest end-use, and it in turn is several times larger than any of the remaining end-uses. Since MECS
1469
provides data on commercial fuels and electricity for machine drive, the major differences between the machine drive values in the three studies is due to the different techniques to allocate ‘‘Other’’ fuels, recovered waste heat, and steam to machine drive. The remaining eight end-uses collectively account for less than 6% of the end-use energy in all three studies. 4.4. Significant contributions and opportunities for further work This energy end-use model uses an analysis of onsite steam and power generation in the petroleum industry based on actual operating performance taken from the EIA 860B database, whereas earlier models either made assumptions or used operating data from few plants. This is also the first study that combined the EIA 860B and MECS databases to estimate waste heat recovery in this industry, and we have pioneered the use of 860B data to allocate recovered waste heat to end-uses. Also, for the first time, a two-phase procedure involving key refining processsteps was used to allocate steam to end-uses. Finally, this is the first end-use study that assessed the accuracy of the MECS database for the petroleum industry. A significant improvement in end-use modeling would be possible if MECS were expanded to allow inclusion of data on end-use allocations of steam and ‘‘Other’’ fuel in Table 2. Of particular value for modeling the petroleum industry would be the quantification of steam produced from high temperature refining processes in addition to the currently available information on steam produced in central boilers. Data on cascading of waste heat from high temperature processes to low temperature processes, other end-uses, or bottoming cycles for power generation could also be integrated into end-use models. These refinements would increase the ability to make end-use allocations on the basis of statistically sound data, instead of the modeling assumptions such as those made in Section 3.8 of this paper. Such developments would greatly enhance the ability of end-use models to serve as calibration tools for process-step models such as [27]. Even in the absence of additional MECS data, the same approach used in this paper can be used to create energy end-use models using MECS data for 1991, 1994, and 2002 to compare with our 1998 model. This will help to reveal trends and can also be used to calibrate energy-forecasting models. Acknowledgment This research was supported by US Department of Energy through Pacific Northwest National Laboratory under Task Order 11457 of Master Agreement 6630, and through a research agreement with the American Institute of Chemical Engineers. We would like to thank to Dr. Joseph M. Roop of Pacific Northwest National Laboratory for his assistance.
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References [1] US Department of Energy. Energy Information Administration, Petroleum industry analysis Brief, 1998. See also: /http://www.eia. doe.gov/emeu/mecs/iab98/petroleum/index.htmlS. [2] Manufacturing Consumption of Energy 1998. Washington, DC: US Department of Energy, Energy Information Administration, 1998 Energy consumption by manufacturers. Energy consumed as fuel by manufacturing industry and region. See also: /http://www.eia.doe. gov/emeu/mecs/mecs98/datatables/contents.htmlS. [3] Dincer I. The role of exergy in energy policy making. Energy Policy 2002;30:137–49. [4] Hyman B, Reed T. Energy intensity of manufacturing processes. Energy 1995;20:593–606. [5] Worrell E, Phylipsen D, Einstein D, Martin N. Energy use and energy intensity of the US chemical industry. Ernest Orlando Lawrence Berkeley National Laboratory Report 2000; LBNL-44314. See also: /http://ies.lbl.gov/iespubs/44314.pdfS [6] Patel M. Cumulative energy demand (CED) and cumulative CO2 emissions for products of the organic chemical industry. Energy 2003;28:721–40. [7] Ayres R, Ayres LW, Warr B. Exergy, power and work in the US economy 1900–1998. Energy 2003;28:219–73. [8] Utlu Z, Hepbasli A. Turkey’s sectoral energy and exergy analysis between 1999 and 2000. Int J Energy Res 2004;28: 1177–96. [9] Rosen MA, Dincer I. Exergoeconomic analysis of power plants operating on various fuels. Appl Thermal Eng 2003;23: 643–58. [10] Giraldo L, Hyman B. Energy end-use models for pulp, paper, and paperboard mills. Energy 1995;20:1005–19. [11] Ozalp N, Hyman B. Energy end-use model of paper manufacturing in the US. Appl Thermal Eng 2006;26:540–8. [12] Andersen JP, Hyman B. Energy and material flow models for the US steel industry. Energy 2001;26:137–59. [13] Arthur D. Little (ADL). Overview of energy flow for industries in standard industrial classifications. Report to the US Department of Energy Office of Industrial Technology 2000; DOE/OIT–71563. [14] US Department of Energy. Energy Efficiency and Renewable Energy (EERE), Energy footprints 2004. See also: /http://www.eere.energy. gov/industry/energy_systems/footprints.htmlS. [15] US Department of Energy. Manufacturing Energy Consumption Survey (MECS), 1998. See also: /http://www.eia.doe.gov/emeu/ mecs/contents.htmlS. [16] US Department of Energy. Energy Information Administration (EIA) 1998, EIA 860B database. See also: /http://www.eia.doe.gov/ cneaf/electricity/page/eia860b.htmlS.
[17] Ozalp N, Hyman B. Calibrated models of onsite power and steam production in US manufacturing industries. Appl Thermal Eng 2005;26:530–9. [18] The DOE Industrial Assessment Database. Retrieved December 2005, from /http://iac.rutgers.edu/technicaldocs/dbman_82.phpS. [19] Chem-Intell Chemical Manufacturing Plants. Retrieved December 2005, from /http://oaspub.epa.gov/pls/lcaccess/LCA.OTHER_SRCSS. [20] Annual Energy Review. Retrieved December 2005, from /http:// www.eia.doe.gov/emeu/aer/contents.htmlS. [21] EIOLCA. What are the data sources for the eiolca.net software. Retrieved December 2005, from /http://www.eiolca.net/methods.htmlS. [22] Industry Economic Accounts. Retrieved December 2005, from /http://www.bea.gov/bea/dn2/iedguide.htmS. [23] North American Industrial Database. Retrieved December 2005, from /http://www.industrialinfo.com/dbprodsnaics07.jspS. [24] Major Industrial Plant Database. Retrieved December 2005, from /http://www.ihsenergy.com/products/mipd/index.jspS. [25] Annual Survey of Manufactures. Retrieved Feb. 2006 from /http:// www.census.gov/mcd/asmhome.htmlS. [26] Manufacturing energy consumption survey methodology: survey design. Manufacturing Consumption of Energy (MECS), 1994. Report ID#: DOE/EIA-0512(94), December 1997. Implementation, and Estimates. See also: /http://www.eia.doe.gov/emeu/mecs/methodology/methodology_mecs94.htm#The%20Sampling%20Frame%20and%20Its%20Relationship%20to%20the%20Manufacturing%20SectorS. [27] Brown HL, et al. Energy analysis of 108 industrial processes. Fairmont Press; 1996. p. 227–31. [28] Wang L, Lee H, Molburg J. Allocation of energy use in petroleum refineries to petroleum products: implications for life cycle energy use as an emission inventory of petroleum transportation fuels. Int J Life Cycle Assess 2004;9:34–44. [29] Chiogioji M. Industrial energy conservation. New York: Marcel Dekker; 1979. [30] Energy and Environmental Analysis, Inc. The petroleum refining industry, vol. 3 of a report prepared for the Mellon Institute under DOE Contract DE-AC01-79CS-40151. September 1982. [31] Resource Dynamics Corp. Steam system opportunity assessment for the pulp and paper, chemical manufacturing, and petroleum refining industries, DOE/GO-102002-1639, October 2002. [32] Worrell E, Galitsky C. Profile of the petroleum refining industry in California. Lawrence Berkeley National Laboratory, LBNL-55450 March 2004. [33] Energetics, Inc. Energy and environmental profile of the US petroleum refining industry, December 1998. [34] Personal conversation with Professor Levent Ballice, Chemical Engineering Department, Ege University, Izmir, Turkey.
Energy 32 (2007) 1460–1470 www.elsevier.com/locate/energy
Allocation of energy inputs among the end-uses in the US petroleum and coal products industry Nesrin Ozalp,1, Barry Hyman Mechanical Engineering Department, University of Washington, Box 352600, Seattle, WA 98195, USA Received 13 September 2005
Abstract This paper models the allocation of energy inputs in the US petroleum and coal products industry by allocating combustible fuel and renewable energy inputs among generic end-uses, including intermediate conversions through onsite power and steam generation. This analysis, called an energy end-use model, showed that 72% of the fuel input in the US petroleum and coal products industry goes to onsite steam and power generation, whereas 28% goes directly to end-uses. Eight percent of the boiler output is used for power generation, 72% goes directly to end-uses, and 20% is waste heat. Among the end-uses, process heating is the biggest energy user with a total energy consumption of 2338 PJ, whereas machine drive is the biggest electricity consumer with a consumption of 168 PJ. This paper also provides estimates of the uncertainty of the data. The approach to create this model is applicable to all other industries for which data is available and the model is consistent with US Department of Energy data for 1998. When used in conjunction with similar models for other years, it can be used to identify the changes and trends in energy utilization even at the prime mover level of detail. r 2006 Elsevier Ltd. All rights reserved. Keywords: Energy; Allocation; End-use; Petroleum industry; United states
1. Introduction The US petroleum and coal products industry is the largest refined petroleum products producer in the world and the largest energy consuming industry in the US [1]. The petroleum refining subsector of the US petroleum and coal products industry accounts for 96% of the industry’s energy consumption in 1998 [2]. The objective of this paper is to provide a detailed energy end-use model of the US petroleum and coal products industry. Such a model provides energy flows for onsite power and steam, and end-uses such as heating ventilating and air conditioning (HVAC), machine drive, lighting, etc. An energy end-use model can help identify opportunities to improve energy efficiencies and enhance energy policies [3]. It can also serve as key for other studies such as energy Corresponding author. Tel.: +90 546 439 8880; fax: +90 232 388 8562.
E-mail address: [email protected] (N. Ozalp). Current address: Ege University, Mechanical Engineering Department, 35100 Bornova, Izmir, Turkey. 1
0360-5442/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2006.09.006
process-step models [4–6], energy cost and exergy analysis for manufacturing industries [7–9]. There have been a number of prior efforts to study energy end-uses in manufacturing industries such as: paper industry end-use models using 1991 data [10] and 1998 data [11]; a steel industry combined end-use and process-step model for 1994 [12]; energy end-use models for several manufacturing industries for 1994 [13] and 1998 [14]. In this paper, we follow the same modeling approach used in [11]. However, in this paper, we go beyond that work by assessing the accuracy of the data, and employing a more sophisticated approach to allocating steam and waste heat to end-uses.
2. Databases The primary data source used to construct energy enduse models is the manufacturing energy consumption survey (MECS) issued by the Energy Information Administration (EIA) of the US Department of Energy. The
ARTICLE IN PRESS N. Ozalp, B. Hyman / Energy 32 (2007) 1460–1470
secondary data that was used is EIA 860B: Annual electric generator report for non-utility. A brief description of these and other databases are given in this section.
1461
manufacturing sector. See [26] for additional details on the MECS survey design and implementation. 3. Methodology
2.1. Manufacturing energy consumption survey (MECS) MECS [15] includes data on fuel inputs for heat, power and electricity generation and allocation of inputs to generic end-uses. It also contains data on electricity and steam purchases/sales. The most recent complete MECS data available during the time of this study was 1998. The complete MECS data for 2002 was released in June 2005. MECS uses the North American industrial classification system (NAICS) in order to classify each industry. The NAICS code for the petroleum and coal products industry is NAICS 324. Although MECS provides comprehensive good quality data, as others have noted ‘‘y there are several issues and discrepancies with the MECS data.’’ [13]. In particular, the major gaps in the MECS data are: 1. steam use by component and application is not reported. As an example, a steam turbine is a component, and an example application is electricity generation; 2. steam allocation to end-uses such as process heating is not reported; 3. recovered waste heat is not reported; 4. recovered waste heat allocation to end-uses is not reported; 5. on-site electricity generation by component is not reported; 6. allocation of ‘‘other fuels’’ among components or applications or end-uses is not reported; 7. some data is withheld to protect proprietary information.
Constructing an energy end-use model includes using MECS data to build an energy utilization table (Table 1) and an end-use table (Table 2). These tables show the 95% confidence intervals for the data values based on relative standards of error (RSE) tabulated in MECS. Numerical values less than 0.5 PJ are indicated in Tables 1 and 2 by asterisks. Values denoted by Q represent data withheld by MECS because RSE450%. The details of how to use these tables to assemble an energy end-use model is provided elsewhere [11] and summarized below. 3.1. Filling in the missing values in the tables We begin filling in the missing parts in Tables 1 and 2 by converting asterisks to zero. Then, we can fill in the Q values based on column summation. For example, the value for residual fuel oil used in machine drive in Table 2 should be 2 PJ to make the total direct process uses column summation 33 PJ. Note that some of the columns are unbalanced by about 1 PJ due to round off errors. Table 1 Inputs for heat, power and electricity generation in petroleum and coal products industry, 1998, PJ MECS Source
Energy form
Amount
Table N3.2
Total Net electricity Residual fuel oil Distillate fuel oil Natural gas LPG and NGL Coal Coke and breeze Other
38217157.81 13375.49 7674.93 2472.12 1061756.34 4173.63 * 0 24857175.94
Table N5.1
Total byproducts Blast furnace/coke oven gases Waste gas Petroleum coke Pulping liquor or black liquor Wood chips, bark Wastea
21457131.06 270.05 1476748.56 666731.30 0 0 170.07
Table N13.1
Net demand for electricity Purchases Transfers in Total onsite generation Sales and/or transfers offsite
19574.84 13978.19 * 6171.51 670.15
2.3. Other data sources
Table N13.2
There are various public and private data sources providing information on energy use in the manufacturing sector [18–25] but none of them provide data that can be combined with the MECS and 860B to give a more complete picture of national energy end-use patterns in the
Total onsite generation Cogeneration Renewable energyb Other
6173.48 5872.65 0 470.21
Table N11.3
Purchased steam
9873.15
2.2. Energy information administration’s 860B data (EIA860B): Annual electric generator report for non-utility EIA 860B [16] includes data for all non-utility plants, based on their NAICS codes, whose capacity is at least 1 MW. We used this data for the onsite power and steam generation part of the model. It allows us to calculate electric conversion and waste heat recovery efficiencies at the prime mover level based on actual operating data. The details of these calculations are provided elsewhere [17].
a
Waste including waste oils, tars and waste materials. Renewable energy excluding wood and other biomass.
b
ARTICLE IN PRESS N. Ozalp, B. Hyman / Energy 32 (2007) 1460–1470
1462
Table 2 End-use data for NAICS 324 extracted from MECS Tables N6.2 and N6.4, 1998, PJ MECS end-uses
Net electricity
Residual fuel oil
Distillate oil and diesel fuel
Natural gas
LPG
Coala
Other
Net demand for electricity
Total fuel consumption
13376.8
7677.21
2471.23
1061761.96
4174.19
*
24857NA
195710.76
Indirect uses (Boiler fuel)
170.07
4375.31
370.2
276720.98
770.93
*
—
170.07
Direct uses (Total process uses) Process heating Process cooling and refrigeration Machine drive Electro-chemical processes Other process use Direct uses (Total non-process use) Facility HVAC Facility lighting Facility support Onsite transportation Conventional electricity gen. Other non-process use
12275.55 27NA 570.25 11577.81 * 0 1170.5 470.28 470.03 170.02 * — *
3372.79 317NA 0 Q — 0 0 0 — 0 — 0 0
1570.68 137NA 0 270.14 — 0 570.23 * — * 470.18 * *
705736.66 6887NA 270.11 1371.01 — 170.08 8074.16 870.63 — 670.12 0 6471.43 170.01
3373 277NA 270.2 Q — 0 170.09 * — * * * 0
0 0 0 0 — 0 0 0 — 0 — 0 0
— — — — — — — — — — — — —
17878.54 271.10 870.37 168711.69 * 0 1570.85 770.57 570.05 270.04 * — *
End-use not reported
*
0
Q
*
*
0
24857NA
*
a
Coal excluding coal coke and breeze.
3.2. Fuel and net steam inputs in the energy end-use model
3.6. Allocation of fuels and electricity to end-uses
The fuel inputs from Table 2 are displayed in the lower left corner of Fig. 1. Following the procedure described in detail in [11] and assuming that steam from noncombustible renewables is zero, we conclude that other energy sources except net steam ¼ 2387 PJ and net steam ¼ 98 PJ (displayed in the middle left side of Fig. 1). Note that net steam is not part of onsite steam generation.
End-uses are located on the right side of Fig. 1. The values in Table 2 were used to allocate 808 PJ of commercial fuels and 193 PJ of electricity to these enduses. However, Table 2 does not allocate ‘‘Other’’ fuels to either onsite power and steam generation or generic enduses. But the onsite conversion model based on EIA 860B data and shown in Fig. 2 requires 2590 PJ total fuel input to boilers and prime movers. A backward calculation indicates that 2196 PJ of ‘‘Other’’ should go to for onsite power and steam generation, with the remaining 191 PJ delivered directly to end-uses. Combined with the commercial fuels, that leads to 999 PJ of fuel allocated to end uses. The distribution of commercial fuels among the end uses is provided in Table 2. We assume that the 191 PJ of ‘‘Other’’ fuels is allocated among the end-uses in the same proportion as the commercial fuels. See Fig. 3 for an explicit allocation of fuels to these end-uses.
3.3. Offsite electricity The acquisition and disposition of electricity is presented in the upper left corner of Fig. 1 as purchased electricity, electricity sold, and electricity from noncombustible renewables. These values are taken from Table 1. 3.4. Onsite power and steam generation A condensed version of our onsite power and steam generation model for the petroleum industry is shown in the upper middle portion of Fig. 1, and includes 61 PJ of onsite electricity, 1784 PJ of boiler steam for end-uses, and 156 PJ of recovered waste heat. The detailed model, which was created elsewhere [17] from EIA 860B 1998 data is shown in Fig. 2. 3.5. Steam loss We assumed an average 30% energy loss during steam distribution due to heat transfer, ineffective steam traps, leaks etc. as was done in [11].
3.7. Allocation of recovered waste heat to end-uses In contrast to commercial fuels and electricity allocations to end-uses, there is no MECS data for allocating recovered waste heat to end-uses. However, EIA 860B provides data on the amount of waste heat recovered and provides some descriptive information on the applications of recovered waste heat. This information is summarized in the Table 3 column ‘‘Original data’’. When there are multiple applications for waste heat in a given facility (see the last five rows of Table 3), 860B identifies the multiple applications but does not quantify the allocations among those applications. To resolve this ambiguity, we assume
ARTICLE IN PRESS N. Ozalp, B. Hyman / Energy 32 (2007) 1460–1470 139
Purchased Electricity
1463
194
55 6
Electricity Sold
Unrecovered Waste Heat 61
Electricity from Noncombustible Renewables
0
586
1
257
Onsite Electricity Generation
156 Waste Heat
62 380 491
2261
2 Process Heating
185
195
Boiler
2395
5 1784
98 Net Steam
Process Cooling and Refrigeration
8
2590 Distribution Losses 43 76
168
64 Machine Drive
Export 565
3
33
0
Electro-Chemical Processes
107
Other Process Use
Residual Fuel Oil 153
1317
0
3 Distillate Fuel Oil
21
24
341 Natural Gas
720
1061
999 Liquified Petroleum Gas
41
0
0
2316
2469
193 17
7 Facility HVAC
7
34 0
0
0
5 Facility Lighting
Coal 0
0
72
0
Facility Support
Coke and Breeze Other Energy Sources Except Net Steam
2387
2196
191
50
Onsite Transportation
10
Other NonProcess Use
Fig. 1. Energy end-use model of NAICS 324 in 1998, PJ.
that process steam accounts for 60% of recovered waste heat when there are dual applications, and 50% of recovered waste heat when there are three or more applications; with the remainder divided equally among the non-process heat applications. This allows us to
subdivide the multiple applications and adjust the first five rows at the top of Table 3 to obtain reassigned allocations. Note that the 860B term ‘‘application’’ used in Table 3 is not the same as the MECS term ‘‘end-use’’. In particular, we interpret the process steam application values in Table 3
ARTICLE IN PRESS N. Ozalp, B. Hyman / Energy 32 (2007) 1460–1470
1464
Non combustible renewables
0
Electricity generated Unrecovered waste heat
63
<1
1
46
87
Steam turbine
Gas turbine
ICE
586
17 168
Electricity input
Waste heat 491
156
Recovered waste heat to end-uses
1
194
1
62
185 2395
Boiler
2590
1784
Fuel input
Steam to end-uses
Fig. 2. On-site power and steam generation model of NAICS 324 in 1998, PJ.
to be recovered waste heat in the form of steam. To test the reasonableness of this approach, we use the results from [17] for the amount of waste heat recovered from on-site power generation in steam turbines and combined cycle plants, and assume that all of that recovered waste heat is in the form of steam. The steam turbine with heat recovery is modeled in Fig. 4, taken from Fig. 3 in [17]; the combined cycle is modeled in Fig. 5, taken from Fig. 4 in [17]. From the numerical values listed in Tables 3 and 6 in [17], we obtain
temperature process heating activity. If credible data on the amount of this energy transfer were available, we could divide the process heating end-use box in Fig. 3 into hightemperature process heating and low-temperature process heating boxes and show the energy flow between them. Another example of recovered waste heat not included in our analysis is the use of waste heat from process heating end-use for use in another end-use such as facility HVAC. Again, if data were available, this energy transfer could be depicted by an arrow connecting the two end-uses in Fig. 3.
Waste heat recovered as steam
3.8. Allocation of steam to end-uses
¼ x14 þ x25 ¼ 71 þ 58 ¼ 129 PJ. So the reassigned process steam allocation value in Table 3 is within 13% of the value in [17] for waste heat recovered as steam from onsite cogeneration. Hence, we will add the 112 PJ of reassigned process steam in Table 3 to the 1317 PJ of net steam and boiler output (after distribution losses) and allocate it to end-uses as described in the next section. Since the waste heat recovered as steam is described in 860B as ‘‘net useful thermal energy’’, we will not apply the steam distribution loss factor to this amount. The reassigned value of 3 PJ waste heat in Table 3 is depicted as Exports in Fig. 1. The remaining reassigned allocations of waste heat are associated with the end-uses listed in the last column of Table 3, and displayed explicitly in Fig. 3. The recovered waste heat discussed in this section consists of waste heat recovered from fuel fired boilers and onsite power generators. Not included in this analysis is waste heat that may be recovered from an end-use. For example, waste heat from a high-temperature process heating activity may be recovered for use in a low-
In this section, we allocate the 1317 PJ of net steam and boiler steam (see middle bottom of Fig. 1) and the 112 PJ of waste heat recovered as steam (from Table 3) to the enduses. Here we are faced with a challenge similar to the one described in the previous section—there is no MECS data on allocation of steam to end-uses. Allocating steam to end-uses is even more difficult because, unlike the case with recovered waste heat, 860B provides no insight into the allocation of boiler steam or net steam. Hence, we turn to process-step models of petroleum refining for insights into end-uses of steam. However, process-step models of industrial processes inherently involve many assumptions about operating parameters such as temperatures, pressures, etc. for each step in a multi-step manufacturing process. In addition, multiple processes are frequently available to produce a given product. Many of these process-step details involve proprietary information, and we are unaware of any statistical-based representative quantitative model of petroleum refining in the open literature. In fact, one of the major values of an end-use model of petroleum refining based on MECS and 860B data is to use it to calibrate
ARTICLE IN PRESS N. Ozalp, B. Hyman / Energy 32 (2007) 1460–1470
939 1310 12
Process Heating
5 0 0
Process Cooling and Refrigeration
26 38 0
Machine Drive
168
0 0 0
Electro-Chemical Processes
0
1 82 24
Other Process Uses
1465
Table 3 EIA 860B data for recovered waste heat in the petroleum industry
2
860B applications
8
Original data
Reassigned allocations
MECS end-use
see Section 3.8 of text Process heating Facility HVAC
Process steam (PS)
95
112
Direct heat (DH) Space heat (SH) Other end users (EU) Other (OT) PS+DH PS+SH PS+EU PS+SH +DH PS+SH+OT+EU
8 0 0 23 5 5 5 8 8
12 5 3 24 — — — — —
156
156
Total
Other process uses
999 unrecovered waste heat
0
1429
193 12 0 5
x11
Facility HVAC
7 steam turbine 5
41 0 0 0
x15
Facility Lighting
waste heat 7
x13
5
x14
to end-uses
x17 x12
7 0 0
Facility Support
5 0 0
Onsite Transportation
boiler 6
2 x16
Fig. 4. Model of power generation and waste heat recovery from steam turbine.
0
unrecovered waste heat Other NonProcess Uses
x18
0
1 0 0
fuel steam recovered waste heat electricity Fig. 3. Allocation of fuel, steam, waste heat and electricity among end-uses in NAICS 324 in 1998, PJ.
gas turbine 8
x21 steam turbine 9
x26
x23
x19
waste heat 11
x25
to end-uses
x24 x22 x20
x27
boiler 10
Fig. 5. Model of power generation and waste heat recovery from combined cycle.
a process-step model of petroleum refining to make the process-step model consistent with national data. Nevertheless, in the absence of national data on steam allocation to end-uses in this industry, we turn to processstep models of petroleum refining to gain at least some insight into end-uses of steam.
3.8.1. Steam usage in refining processes The most detailed quantitative process-step model of petroleum refining was developed in the late 1970s at Drexel University [27]. The Drexel model provides mass
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about the same time as [27]. The estimates presented in [30] are in terms of unit input to each process; we combined them with the input values from [29] to get the values shown in Table 4. Steam consumption patterns from two more-contemporary analyses of energy use in petroleum refining [31,32] are also shown in Table 4. None of these studies [29–32] contain the detailed mass and energy balances at each process-step that are included in [27] so they are not true process-step models in the sense that [27] is. Another contemporary study [33] does not explicitly identify steam consumption for many process steps, instead indicating qualitatively that energy consumption for some processes include purchased steam and expressing energy consumption in terms of the fuels used to produce steam onsite. Since most of these fuels are also consumed directly in end-uses (see energy flows from left to right at the bottom of Fig. 1) there is no way to distinguish in [33] between the direct use of these fuels and the amount of steam generated by these fuels. However, [33] does explicitly quantify (on a national basis for 1996) steam consumption for fluid coking, and flexicoking; it also quantifies steam production from fluid coking, flexicoking, fluid catalytic cracking, catalytic hydrocracking, catalytic reforming, and catalytic hydrotreating. These processes are listed in Tables 4, but since total steam consumption is not known, we cannot use [33] to provide a distribution of steam consumption among all process steps.
and energy balances at each process step. Although much of the quantitative estimates are based on anecdotal information, and the model has never been updated to reflect new data and changes in process design and technologies, it continues to be used as the reference process-step model for petroleum refining [28]. The petroleum refining process steps identified in [27] that consume steam and the percentage of total steam consumed in each step are listed in Table 4 (we use percentage of steam consumption because it is less likely to change from year-to-year than the amount of steam consumed). The Drexel model also identifies processes that produce steam. The end-use model in Fig. 1 only accounts for purchased steam, steam produced in a boiler from fuel for power generation or direct distribution to end-uses, and steam recovered from onsite power generation via a steam turbine or combined cycle. Steam produced in one industrial process and used in another industrial process is not depicted in Fig. 1. This is similar to the situation discussed in the previous section with respect to recovered waste heat, in that steam generated in one end-use for use in another end-use could be depicted in a more detailed version of Fig. 3 if credible data were available. In the absence of such data, we choose not to include steam production from industrial processes in our end-use model. Several other estimates of steam use in petroleum refining [29,30] also shown in Table 4 were published
Table 4 Comparison of steam consumption patterns in petroleum refining processes (%) Process category
Process
Drexel [27]
Chiogioji [29]
EEA [30]
Resource dynamics [31]
LBL [32]
Atmospheric distillation Vacuum distillation Thermal cracking Visbreaking Lube oil processing Asphalt processing Fluid catalytic cracking
9 10
16 13 4 1 p.n.i. p.n.i.
35 32
27 14
24 13
o1* p.n.i p.n.i 0
0 0 0
15 1
1*
Coking Delayed coking Fluid coking Flexicoking Catalytic hydrocracking Catalytic hydrotreating Catalytic reforming Distillate hydroforming Hydrogen treating Alkylation Isomerization
53 p.n.i. p.n.i.
5 1 p.n.i. 1 16
3 2 2
5
p.n.i. p.n.i. p.n.i.
30
2
4 23 13 p.n.i. p.n.i 15
2 1
1 n.e.i.
1 3
25 0
1 Isobutane Isopentane/isohexane Gas recovery Other Total
p.n.i. process not included in model. n.e.i. amount of steam not explicitly identified. * not included in total; see text.
4 27 10 p.n.i 0 12 4
5
10
100
100
p.n.i. 100
100
0 6 100
ARTICLE IN PRESS N. Ozalp, B. Hyman / Energy 32 (2007) 1460–1470
Table 4 indicates that [31,32] are in very close agreement on steam consumption patterns, and provide a more contemporary picture of steam consumption than either [27,29,30]. Therefore, we will assume that the total steam utilization for petroleum refining in 1998 was distributed among refining processes according to the pattern described in [31]. Though [31] does show a small amount of net steam production for coking operations, we combined the value in [31] with data in [33] to show that the steam input to coking operations accounts for less than 1% of all steam consumption reported in [31]. We therefore, neglect steam input for coking operations, and use the same logic to neglect a similarly small (but unquantifiable) steam input for visbreaking. 3.8.2. Assignment of process steps to end-uses The next step in allocating steam to end-uses is to map the process steps listed in Table 4 to the MECS end-uses. Then the steam distribution among process steps, as determined in Table 4, can be assigned to end-uses. Unfortunately, this mapping is complicated by the fact that many petroleum refining processes involve multiple end-uses. We begin with Table 5 that matches the MECS end-uses with the purposes for which steam is used in petroleum refining as described in [31]. Thus, it is clear from Table 5 that steam use in petroleum refining should be allocated to four MECS end uses: process heat, process cooling and refrigeration, machine drive, and other process uses. The remaining task is to quantify this allocation. Many of the steam consuming processes listed in Table 4 (plus non-steam consuming processes not included in Table 4) require machine drive to operate those processes. However, none of the studies described in [27,29–33] quantify these machine drive requirements, much less indicate how much of that machine drive is in the form of electric motors, diesel engines, gas turbines, or steam turbines. The only information we have been able to identify regarding steam for machine drive in the petroleum industry is that ‘‘steam turbines account for a large amount of energy used in machine-drive applications.’’ (p. 54 in [31]). While MECS provides data on electricity and fuel for
Table 5 Correspondence between purposes for steam use [31] and MECS end-uses Purposes for steam use [31]
Corresponding MECS end-use
Stripping Fractionation Power generation
Other process use Other process use Onsite electricity generation—but this is not an enduse; it is an intermediate use accounted for elsewhere in the model (see upper left part of Fig. 1). Machine drive Process cooling and refrigeration Other process use Process heating Other process use
Mechanical drive Quenching Dilution Process heating Vacuum draw
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machine drive, it does not contain data on steam use for machine drive. Hence, we will assume that machine-drive accounts for the same percent of all steam consumed in enduses as it does for fuel used for process end-uses. Based on Fig. 3, we therefore conclude that machine-drive accounts for 26=ð939 þ 5 þ 26 þ 1Þ ¼ 2:7% of steam consumed in end-uses. This amounts to 38 PJ. The remaining 97.3% (1391 PJ) is divided among process heat, process cooling and refrigeration, and other process uses. Quantitative estimates for allocating this 1391 PJ of steam to these end-uses were provided by [34]. Combining those estimates with the information from [31] in Table 4 leads to the two-step assignments displayed in Fig. 6. The results for steam consumption in end-uses also are shown in Fig. 3, and contribute to the condensed version of Fig. 3 shown in the lower right corner of Fig. 1. 4. Results and discussion This paper presents an energy end-use model for the petroleum and coal products industry (NAICS 324) based on 1998 MECS and EIA 860B data. The model improves in several respects on other end-use models conducted for this and other industries [10–14]. 4.1. Allocation of inputs to end-uses As shown in Fig. 1, 72% of the fuel input goes to onsite steam and power generation whereas 28% goes directly to end-uses. Approximately 92% of the fuel for onsite steam and power goes through the boiler and 8% goes directly to onsite electricity generation. Seventy-two percent of the boiler output goes directly to end-uses, whereas 20% goes to waste heat and the rest to onsite electricity generation. We also see that the contribution of the boiler to the enduses is 18 times greater than that of net steam. Although 1882 PJ goes to end-uses from the boiler and net steam combined, 30% of this amount is lost due to distribution. There is no renewable energy utilization for on-site electricity generation. Onsite electricity accounts for 28% of the total electricity to end-uses, with purchased electricity contributing the remainder. Six PJ of electricity generated onsite is sent to the grid. We see that 2469 PJ is supplied to end-uses by fuel, steam, and recovered waste heat while only 193 PJ is supplied to end-uses by electricity. Process heating is the biggest energy consuming end-use, followed by machine drive. Energy input to the other enduses is considerably smaller. As seen in Fig. 3, the dominant contributors to process heating are fuel (41%) and steam (58%). Electricity accounts for 72% of the input to machine drive, with fuel and steam contributing 11% and 16%, respectively. Machine drive consumes 87% of the electricity furnished to end-uses, with the remainder scattered over five other end-uses.
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Processes
End-Uses
Atmospheric Distillation 37 6 Vacuum Distillation 188 376
1305 Process Heat
1
Fluid Catalytic Cracking
46 188 Catalytic Hydrocracking
324
1 51
Catalytic Hydrotreating
5
161
324
1429
Steam
179
Catalytic Reforming
Other
38
Machine Drive
213
213 59
82 18
59 Alkylation
Isomerization
Fig. 6. Allocation of steam among processes and end uses in NAICS 324 in 1998, PJ.
4.2. Accuracy of the model The fuel and electricity inputs and their allocations to end-uses are based on 1998 MECS data. As displayed in Tables 1 and 2, this data is of high quality. With few exceptions, the 95% confidence intervals are less than 10% of the nominal values. Of even higher quality is the portion of the model that depicts onsite electricity and steam generation, including waste heat recovery, which is based on EIA 860B data for all operating onsite power plants 41 MW in NAICS 324 [17]. Several key assumptions, namely those for steam boiler efficiency (80%) and steam distribution losses (30%), are reasonable estimates based on values appearing in the literature. As shown in Table 3, EIA 860B provides unambiguous allocation of 81% of the recovered waste heat from onsite power and steam production to process steam and to three MECS end-uses. We made reasonable allocations for the remaining 19% in a manner that the reassigned recovered waste heat in the form of process steam was within 13% of the 860B data for recovered waste heat from steam turbines and combined cycle plants. The allocation of steam (from boilers, net steam, and recovered waste heat in the form of process steam) to enduses required a two-phase assignment process depicted in Fig. 6. The first phase involved distributing steam among refinery processes using the estimates in [31]. The second phase involved mapping the steam use in each process to one or more of the generic end-uses. Lacking any published information on this mapping, we relied on expert judgment [34]. In addition, we assumed that the proportion of steam
used for machine drive was equal to the proportion of fuel used for machine drive. 4.3. Comparison of the results with other energy end-use models Table 6 compares our energy end-use model with two other energy end-use models for the petroleum industry [13,14]. This comparison shows that the fuel consumption presented in this study is 1% higher than that given in [14], and 9% higher than that given in [13]. The reason for the difference between the fuel consumption reported in this study and [14] is because we modeled NAICS 324 while [14] models the NAICS 324110 subsector of NAICS 324. The reason for the difference between this study and [13] is because we used 1998 MECS data and [13] used 1994 MECS data. While the steam production estimate provided in this study is reasonably close to that in [13], it is 2.3 times more than the estimates provided in [14]. This difference can be attributed to the assumption made in [14] that most of the ‘‘Other’’ energy sources are used directly for end-uses, while we had over 90% of ‘‘Other’’ going to onsite steam and power generation to satisfy the fuel input requirements of the onsite steam and power production model. Since the onsite steam and power generation model is based on actual operating data contained in 860B, our results are deemed to a better representation of actual steam production than [14]. For the same reason, our estimate for the process heating end-use is somewhat less than that in both [13,14]. The major difference in the distribution
ARTICLE IN PRESS N. Ozalp, B. Hyman / Energy 32 (2007) 1460–1470 Table 6 Comparison of fuels, steam, electricity and losses, PJ This study, 1998
Energy footprints [14], 1998
Fuels Residual fuel oil 76 Distillate fuel oil 24 Natural gas 1061 Liquefied petroleum gas 41 Coal 0 Coke and breeze 0 Other energy sources 2387 Conventional fuel — purchase Other fuels — Total fuel purchase/ 3589 consumption Steam Steam total Electricity Onsite electricity generation Electricity from renewables Electricity sold Electricity purchase Cogenerated electricity Conventional electricity End-uses Process heating Process cooling Machine drive Electro-chemical processes Other process use End-uses HVAC Lighting Facility support Onsite transportation Other non-process use Losses Unrecovered heat Distribution losses
Little [13], 1994
— — — — — — — —
— — — — — — — 1012
— 3540
2257 3269
2067
896
2252
61
55
53
0
—
0
6 139 — —
— 130 — —
7 135 52 1
2261 13 232 0
2898 22 188 —
2804 16 268 0
107
—
30
24 5 9 5 1 586 565
9 > = > ;
8 > <
3 —
14 5 2 2 1
— 480
— —
53
> :
losses (primarily steam) between this study and [14] are also the result of this study estimating more than twice as much steam production as [14], plus [14] assuming a 20% steam distribution loss compared to our 30%. The electricity generation for all three studies are in close agreement, with the differences again attributable to the difference in NAICS for [14] and year for [13]. In terms of end-uses, all three studies show process heating being the dominant end-use by far, accounting for between 85% (this study) and 92% [14] of energy delivered to end-uses. All three studies also agree that machine drive is the next largest end-use, and it in turn is several times larger than any of the remaining end-uses. Since MECS
1469
provides data on commercial fuels and electricity for machine drive, the major differences between the machine drive values in the three studies is due to the different techniques to allocate ‘‘Other’’ fuels, recovered waste heat, and steam to machine drive. The remaining eight end-uses collectively account for less than 6% of the end-use energy in all three studies. 4.4. Significant contributions and opportunities for further work This energy end-use model uses an analysis of onsite steam and power generation in the petroleum industry based on actual operating performance taken from the EIA 860B database, whereas earlier models either made assumptions or used operating data from few plants. This is also the first study that combined the EIA 860B and MECS databases to estimate waste heat recovery in this industry, and we have pioneered the use of 860B data to allocate recovered waste heat to end-uses. Also, for the first time, a two-phase procedure involving key refining processsteps was used to allocate steam to end-uses. Finally, this is the first end-use study that assessed the accuracy of the MECS database for the petroleum industry. A significant improvement in end-use modeling would be possible if MECS were expanded to allow inclusion of data on end-use allocations of steam and ‘‘Other’’ fuel in Table 2. Of particular value for modeling the petroleum industry would be the quantification of steam produced from high temperature refining processes in addition to the currently available information on steam produced in central boilers. Data on cascading of waste heat from high temperature processes to low temperature processes, other end-uses, or bottoming cycles for power generation could also be integrated into end-use models. These refinements would increase the ability to make end-use allocations on the basis of statistically sound data, instead of the modeling assumptions such as those made in Section 3.8 of this paper. Such developments would greatly enhance the ability of end-use models to serve as calibration tools for process-step models such as [27]. Even in the absence of additional MECS data, the same approach used in this paper can be used to create energy end-use models using MECS data for 1991, 1994, and 2002 to compare with our 1998 model. This will help to reveal trends and can also be used to calibrate energy-forecasting models. Acknowledgment This research was supported by US Department of Energy through Pacific Northwest National Laboratory under Task Order 11457 of Master Agreement 6630, and through a research agreement with the American Institute of Chemical Engineers. We would like to thank to Dr. Joseph M. Roop of Pacific Northwest National Laboratory for his assistance.
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[17] Ozalp N, Hyman B. Calibrated models of onsite power and steam production in US manufacturing industries. Appl Thermal Eng 2005;26:530–9. [18] The DOE Industrial Assessment Database. Retrieved December 2005, from /http://iac.rutgers.edu/technicaldocs/dbman_82.phpS. [19] Chem-Intell Chemical Manufacturing Plants. Retrieved December 2005, from /http://oaspub.epa.gov/pls/lcaccess/LCA.OTHER_SRCSS. [20] Annual Energy Review. Retrieved December 2005, from /http:// www.eia.doe.gov/emeu/aer/contents.htmlS. [21] EIOLCA. What are the data sources for the eiolca.net software. Retrieved December 2005, from /http://www.eiolca.net/methods.htmlS. [22] Industry Economic Accounts. Retrieved December 2005, from /http://www.bea.gov/bea/dn2/iedguide.htmS. [23] North American Industrial Database. Retrieved December 2005, from /http://www.industrialinfo.com/dbprodsnaics07.jspS. [24] Major Industrial Plant Database. Retrieved December 2005, from /http://www.ihsenergy.com/products/mipd/index.jspS. [25] Annual Survey of Manufactures. Retrieved Feb. 2006 from /http:// www.census.gov/mcd/asmhome.htmlS. [26] Manufacturing energy consumption survey methodology: survey design. Manufacturing Consumption of Energy (MECS), 1994. Report ID#: DOE/EIA-0512(94), December 1997. Implementation, and Estimates. See also: /http://www.eia.doe.gov/emeu/mecs/methodology/methodology_mecs94.htm#The%20Sampling%20Frame%20and%20Its%20Relationship%20to%20the%20Manufacturing%20SectorS. [27] Brown HL, et al. Energy analysis of 108 industrial processes. Fairmont Press; 1996. p. 227–31. [28] Wang L, Lee H, Molburg J. Allocation of energy use in petroleum refineries to petroleum products: implications for life cycle energy use as an emission inventory of petroleum transportation fuels. Int J Life Cycle Assess 2004;9:34–44. [29] Chiogioji M. Industrial energy conservation. New York: Marcel Dekker; 1979. [30] Energy and Environmental Analysis, Inc. The petroleum refining industry, vol. 3 of a report prepared for the Mellon Institute under DOE Contract DE-AC01-79CS-40151. September 1982. [31] Resource Dynamics Corp. Steam system opportunity assessment for the pulp and paper, chemical manufacturing, and petroleum refining industries, DOE/GO-102002-1639, October 2002. [32] Worrell E, Galitsky C. Profile of the petroleum refining industry in California. Lawrence Berkeley National Laboratory, LBNL-55450 March 2004. [33] Energetics, Inc. Energy and environmental profile of the US petroleum refining industry, December 1998. [34] Personal conversation with Professor Levent Ballice, Chemical Engineering Department, Ege University, Izmir, Turkey.