Comparative Cost Analysis
for Bulk Chemicals
Based on Fossil and
Renewable Resources
Frederik V. R. Neuwahl
Nummer
Een stageverslag van:
Frederik V. R. Neuwahl
Internal Report: NWS-I-200x-xx
Begeleider/Supervisor: Martin Patel
Copernicus Institute for Sustainable Development and Innovation
Department of Science Technology and Society
Utrecht University
Heidelberglaan 2
3584 CS Utrecht
The Netherlands
ABSTRACT
This paper reports on a comparative cost-analysis study conducted to assess the
profitability, in the mid- to long-term, of a set of bulk chemicals based on renewable
resources as a replacement of traditional bulk petrochemicals. The production of
plastics, solvents and chemical intermediates from renewables is nowadays generally
significantly more expensive than producing them from fossil resources, but recent and
prospected advances in biotechnology can result in decisive cost reductions for the
conversion of biomass into bulk chemicals. Also, soaring fossil resources prices due to
their gradual depletion could be in the future a major driver for opening windows of
opportunity for the development of cost-competitive bio-based replacement bulk
chemicals. This study proposes a general methodology to project profited production
costs of chemicals in relation to fossil feedstock and energy prices and in relation to
technical improvements allowing cost reductions in converting biomass into bulk
chemicals. Based on process economics data from industry sources, this methodology is
applied to a set of bulk petrochemical products and replacement chemicals based on
renewable resources to obtain information on the required increase in oil price required
for bio-based replacement to become cost-competitive. These results are finally
combined with projections, derived from IPCC scenarios, for future energy and
feedstock prices; in this way time windows are identified, in which replacement bulk
chemicals based on renewable resources could attain market competitiveness with their
petrochemical equivalents. It is shown that certain products, such as ethyl lactate as a
replacement of several solvents, polylactides as a replacement of PET, and PTT from
renenwables compared to petrochemical PTT, have concrete potential to become
competitive in a relatively short time (already in year 2010); other replacement options,
such as polylactides for polyethylene, bio-based PTT for PET and bio-based hydrogen
for petrochemical hydrogen, will only become economically viable in a longer time
(indicatively, year 2030) and under the conditions of fossil resource prices stabilising at
high levels; finally, in cases such as acetic acid, despite recent technical advancements
the biosynthetic production route is still far too expensive to compete with the
petrochemical route unless further major technical breakthrough take place.
ii
CONTENTS:
LIST OF ABBREVIATIONS…………………………………………………………ii
1. INTRODUCTION…………………………………………………………………...1
2. PRODUCT CHAIN INVENTORY………………………………………………...4
3. METHODOLOGY…………………………………………………………………..7
3.1 Investment costs……………………………………………………………….10
3.2 Utilities costs……………………………………………………………….….11
3.3 Production cost bases…………………………………………………………12
3.4 Material costs………………………………………………………………….13
3.5 Cost allocation………………………………………………………………...14
4. INPUT SCENARIOS FOR FUTURE COST PROJECTIONS…………………16
4.1 SRES Scenarios……………………………………………………………….17
4.2 Four Scenarios for Fossil Fuel Prices………………………………………..19
4.3 Scenario-related Biomass Use and Price…………………………………….21
5. COMPARATIVE COST ANALYSIS: BULK CHEMICALS FROM FOSSIL vs.
RENEWABLE RESOURCES. PRELIMINARY RESULTS……………………...27
5.1 Solvents………………………………………………………………………...28
5.2 Polymers……………………………………………………………………….31
5.2.1 PTT……………...……………………………………………………….31
5.2.1 PLA……………...……………………………………………………….34
5.3 Materials based on Natural Gas……………………………………………..37
5.2.1 Acetic Acid……...……………………………………………………….31
5.2.1 Hydrogen..……...……………………………………………………….34
6. DISCUSSION AND CONCLUSION……………………………………………..41
APPENDIX I: Approximating Naphtha price………………………………………45
APPENDIX II: Electricity price calculation and Utility prices…………………....46
i
LIST OF ABBREVIATIONS
BEOP: Break Even Oil Price
BEOSP: Break Even Oil-to-Sugar Price
DOE: (United States) Department Of Energy
DMT: Di-Methyl-Terephthalate
ECCP: European Climate Change Programme
ECN: Energy Centre Netherlands
EGBE: Ethylene Glycol Butyl Ether
IIASA: International Institute for Applied System Analysis
IPCC: Intergovernmental Panel on Climate Change
MEK: Methyl-Ethyl-Ketone
NIS: Newly Independent States
NREL: (United States) National Renewable Energy Laboratory
PDO: 1,3-PropaneDiol
PE: Polyethylene
PET: Poly(Ethylene Terephthalate)
PHA: Poly(Hydroxyalkanoates)
PLA: Polylactides
PP: Polypropylene
PTT: Poly(Trimethylene Terephthalate)
SRES: Special Report on Emission Scenarios
TPA: Terephthalic Acid
WEO: World Energy Outlook
WEP: World Energy Perspectives
ii
1. INTRODUCTION
Traditionally, mankind has used materials originating from living organisms (by
definition renewable materials) for a variety of applications: not only as fuel wood but
also as building materials (principally wood), for clothing (cotton and silk fibre, animal
hide, leather), as lubricants (animal and vegetable fats) and as detergents (saponification
of the same fats). The 20th century, owing to the development of large-scale
petrochemistry, has seen the gradual replacement of many of those materials by
cheaper, more durable, better performing products of petrochemical origin.
Nowadays over 99% of all plastics,1 and 96% of bulk chemicals, are produced or
derived from the major non-renewable energy sources (crude oil, natural gas and coal),
which are used both as energy and as feedstock materials in processing. While
agricultural materials have been considered for some time as a replacement energy
source or feedstock substitute for plastics production, in the past decades they have
fallen short of expectations. The main general resistance to their further use has been
cost, followed closely by functionality and lack of flexibility in producing specialised
plastics materials. Nevertheless, as the world enters a century with new priorities for
renewable energy and management of waste, renewed interest was raised in
biomaterials and the efficiency with which they can be produced. Recent advancements
in biotechnology, including genetic engineering, is helping to narrow the cost
differential between petrochemical and renewable-based materials, as well as to
improve material properties. The prospects of producing materials based on renewable
feedstock are in course of extensive re-evaluation, and major chemical companies have
started to invest substantial efforts in related R&D.
This document was prepared in the context of the BREW project2, a public-private
partnership project funded by the EU under the 5th Framework Programme and
coordinated by the Utrecht University. The full title of the BREW project is: “Medium
and long-term opportunities and risks of the biotechnological production of bulk
chemicals from renewable resources”. The overall workplan of the project consists of
ten work packages, covering, inter alia, techno-economic aspects, environmental issues,
Michael, D. “Biopolymers from crops: their potential to improve the environment”, Proceedings of the
11th Australian Agronomy Conference, Geelong, 2003
2
http://www.chem.uu.nl/BREW
1
1
risk analysis, public acceptance. This study is essentially concerned with the technoeconomic aspects of the BREW project.
The first work packages identified and analysed a portfolio of products and
processes that, on the basis of technical specifications and economic prospects, in the
short or mid-term have significant potential to become competitive with existing bulk
chemicals based on petrochemical feedstock. In order to assess the actual profitability
of a biotechnological product, it is necessary to perform a comparative cost analysis
relative to the petrochemical product(s) it is meant to replace.
The central question addressed by this paper is:
Which bulk chemicals based on renewable resources have significant potential
to attain market competitiveness with their petrochemical analogues in the next
two decades?
This central research question is approached by attaining three main objectives:
 To set a methodological framework aimed at projecting the cost of chemical
products in relation to the price of feedstock and energy carriers;
 To develop a tool, based on this methodological framework, to estimate the
future cost of a set of bulk petrochemical products relevant to the BREW
project;
 To make a preliminary assessment of the mid- to long-term potential market
competitiveness of selected bulk chemicals based on renewable resources.
In most cases biotechnological bulk chemicals are, currently, more costly than their
petrochemical analogues, but in the future the situation could change significantly
mainly due to two factors: on the one hand, technological advancements are expected to
cut down significantly the cost of converting renewable biomass into products such as
plastics, solvents, lubricants and surfactants; on the other hand, the gradual depletion of
non-renewable resources will ultimately cause oil and natural gas prices to soar,
resulting in the increase of production cost of petrochemical commodities.
In particular, the Special Report on Emission Scenarios,3 prepared by IIASA for the
Intergovernmental Panel on Climate Change, showed the possibility, under certain
scenarios, of future oil prices stabilising at levels in excess of five times the current
ones. In order to give an indication of the time scale involved in the expected evolutions
of the techno-economic parameters relevant to the present analysis, SRES projections
Nakicenovic, Nebojsa and Swart, Rob (eds.), “SRES: Special Report on Emissions Scenarios”, 2000,
Cambridge University Press, Cambridge, United Kingdom, 612 pages
3
2
are incorporated in the model within the boundaries of some of the scenarios most
commonly in use in the modellers’ community.
Chapter 2 describes the chain of petrochemical products included in the model;
Chapter 3 reports on the methodological framework utilised to calculate the relevant
economic parameters and on the computer model, BREWprofit, implementing that
framework; Chapter 4 introduces the SRES scenarios and their integration with the
comparative cost analysis model; Chapter 5 is concerned with preliminary comparative
cost analysis of selected bulk chemicals from fossil and renewable resources; Chapter 6
discusses the results and briefly concludes.
3
2. PRODUCT CHAIN INVENTORY
A chain of petrochemical products was identified on the basis of equivalence with
the portfolio of base chemicals derived from renewable resources covered by the
BREW project. Equivalence, in the context of this study, can either be chemical or
functional: either the same chemical product can be produced both by a petrochemical
and a bio-based route, or a new product based on renewable feedstock can substitute a
different petrochemical product with similar characteristics. Table 2.I gives an overview
of a series of bio-based products relevant to BREW, and of their petrochemical
equivalents included in this study.
Table 2.I: Biotechnological-to-Petrochemical Product Equivalence
BIOTECH
PETCHEM
Acetic Acid
Acetic Acid
Acrylonitrile
Acrylonitrile
Adipic Acid
Adipic Acid
Ethanol
Ethyl lactate
Ethanol
Glycol ethers (EGBE), MEK, Acetone
Ethylene
Ethylene
Glycerol
Glycerol
Hydrogen
Isopropanol
Mcl-PHA
MEK
Phenol
PLA
Propylene Glycol
PTT
Hydrogen
Isopropanol
Polyethylene, Polypropylene
MEK
Phenol
Polyethylene, PET
Propylene Glycol
PTT, PET
Performing cost-structure analysis on the petrochemical products of table 2.I, in
turn, requires accounting for all synthetic intermediates. Scheme 1 shows the complete
tree of oil and natural gas derived base chemicals included in this study. Intermediate
materials based on inorganic feedstock are not shown in the tree, although they are
included in the model. Such materials are acids (Hydrochloric, Phosphoric and
Sulphuric Acid), bases (Sodium Hydroxide) and Oxidisers (Chlorine and Oxygen).
4
Scheme 1: Petrochemical Product Chain included in this study. 4
C4 fract.
MEK
Acetone
Benzene
Cumene
Aromatics
Phenol
Adipic Acid
p-Xylene
Ethanol
Polyethylene
Ethylene
Et. Oxide
Et. Glycol
PET
Polyprop.
OIL
NAPHTHA
Isopropanol
Glycerol
Propylene
Prop. Oxide
SYNGAS
PDO
PTT
CO
n-Butanol
EOBE
Hydrogen
DMT
NAT. GAS
Methanol
TPA
Acetic Acid
Ammonia
Acrylonitrile
Acrylamide
Nitric Acid
It often happens that several different synthetic routes or industrial processes exist
for the same product. In those cases, the criteria for choosing the representative process
for each product, in descending order of priority, were:
1. Largest installed capacity in the European Union: for instance, since the
majority of European steam-crackers run on naphtha, the model process chosen
for the production of ethylene is that using naphtha as a feedstock, despite the
fact that a range of different feedstock materials are also used (ethane, propane,
natural gas, gas oil, atmospheric distillation residual) and that some of them
offer better profit margins than naphtha,5 if not for unfavourable logistics.
2. Lowest production cost: whenever decisive information on installed capacities
was not readily available, the cheaper process was chosen as representative. On
economic grounds it can be expected that in most cases criteria 1 and 2 tend to
select the same industrial process.
3. Smallest amount (economic value) of by-product credits: In those cases in
which the value of by-product credits is of the same order of magnitude or more
4
In this scheme, PET is obtained from DMT and PTT from TPA. This difference is not related to a
specific synthetic necessity but to the specific process economics reports used as data sources.
5
SRI PEP YEARBOOK 2000, 2M 267-284
5
than the value of the main product, the propagation of errors on the allocation
procedure can generate large errors in estimating the cost of a product that
cannot strictly be regarded as the main output of the process.
6
3. METHODOLOGY
In general, cost analysis is understood as an inquiry to assist decision-makers in
choosing preferred future courses of action by evaluating selected alternatives on the
basis of their costs, benefits, and risks. Specifically, cost analysis in this report should
be understood as a standardised procedure intended as a support tool to assess and
compare the present and future profitability of alternative productive processes for a set
of base chemicals. This is performed by breaking down the production costs into
different components and establishing the relationships between those single aspects
and a set of parameters that are considered as variable in a dynamic (future-oriented)
analysis. For petrochemical products, it is especially important to model the impact of
(feedstock) oil and other energy carriers on production costs, while the cost of biomass
and the optimisation of processes such as the conversion from biomass to fermentable
sugar are key to the economics of products based on renewable resources.
The geographical demarcation of this analysis, consistently with the scope of the
BREW project, is the European Union. As for the time demarcation, the model does
not, at the highest (most abstract) level, include time as an explicit variable, but only
feedstock and energy process. Time appears as a variable as soon as energy and
feedstock price projections are combined into the model as exogenous input. Such input
was mainly derived from SRES scenarios, which run until year 2100. However, the
BREWprofit model is based on a set of reference data, estimates and assumptions that
limit the applicability of all results to a shorter time range; results are presented up to
year 2050. The technical and economic parameters for industrial processes are year
2000 data; hence the year 2000 is chosen as the reference year. The technology of all
petrochemical processes included in this report is assumed to be mature in the reference
year, and all parameters such as investment costs, labour and utility inputs are constant.
The cost analysis procedure was implemented as a self-standing Excel tool
(BREWprofit) structured as follows:

The first spreadsheet calculates the prices of utilities (electricity, steam, cooling
water, etc.) and refinery products (mainly naphtha, which is assumed, together
with natural gas, to be the main feedstock for the European chemical industry;
see Appendix 1 for naphtha price derivation) on the basis of either one, three or
four parameters (cost indices). These indices, which are exogenous input data
7
from the scenarios used for data analysis (see Section 4.2), multiply the
reference (year 2000) values of:
o (Four variables): Oil, Gas, Coal, Electricity.
o (Three variables): Oil, Gas, Coal. The Electricity index is calculated on the
basis of the other three, which can be done readily under the assumption that
the energy mix for electricity production does not change in time.6
o (Single variable): Oil. In this case approximations are made to link the coal
and gas indices to the oil index. Different relationships are implemented
under different scenarios, based on best fit of SRES projections. The use of a
single cost index (which is chosen as the parameter, oil price, having the
largest effect on the cost of petrochemical products) allows calculating
indicators as a function of only one variable, and accordingly for a
significant simplification of data presentation.7

Each of the ensuing spreadsheets calculates the profited cost of one
petrochemical product, by using the utility costs calculated in the first sheet and
referring to other sheets for the costs of intermediates and by-products. The
inclusion of the complete set of intermediates and by-products makes the model
self-consistent. Total profited costs are calculated as a sum of the following
terms:
o Investment costs
o Utilities costs
o Production cost bases
o Material costs
Table 3.I presents an overview of the products/interlinked-spreadsheets included in
the BREWprofit model, including main material inputs, co-products calculated as
output of the same industrial process (see section 3.5) and plant sizes (see section 3.1),
including the inventory of production processes based on renewable feedstock included
in the model for comparative analysis.
6
In a market economy, lower generation cost is a key driver for energy mix change. This approximation
(frozen energy mix) tends therefore to overestimate future electricity costs, up to 10-20%. Since
electricity is in most cases a relatively minor (~1%) cost component for bulk petrochemical products, the
error introduced is in most cases negligible.
7
This approximation is only used for ease and clarity of data presentation, not for scenario-related
projections/calculations.
8
Table 3.I: Inventory of interlinked spreadsheets in BREWprofit.
References: SRI Yearbook 2000, BREW tool (and references therein); see chapter 5.
Plant Size
Main Product
(KT)
Main Inputs
Co-Products
Based on petrochemical resources:
Acetic Acid
Acrylamide
Acrylonitrile
Adipic Acid
n-Butanol
Chlorine
Carbon Monoxide
Cumene
DMT
Hydrogen
Nitric Acid
Phosphoric Acid
Methanol
Ammonia
EGBE
Ethanol
Ethylene
Ethylene Oxide
Ethylene Glycol
Glycerol
Isopropanol
MEK
Polyethylene
PET
Polypropylene
Propylene Oxide
Propylene Glycol
Phenol
PDO
PTT
Sulfuric Acid
Syngas
TPA
Para-Xylene
510
40
360
270
200
400
140
200
500
6
690
660
1690
660
90
540
680
270
170
90
270
60
270
180
250
360
180
180
120
250
960
360
500
240
Carbon Monoxide, Methanol
Acrylonitrile
Propylene, Ammonia
Phenol, Nitric Acid, Hydrogen
Propylene, Syngas
Rock Salt, Electricity
Syngas
Benzene, Proylene
P-Xylene, Methanol
Natural gas
Ammonia
Phosphate Rock, Sulfuric Acid
Natural Gas
Natural Gas
Ethylene Oxide, n-Butanol
Ethylene
Naphtha
Ethylene
Ethylene Oxide
Propylene, Chlorine
Propylene
MTBE raffinate
Ethylene
DMT, Ethylene Glycol
Propylene
Propylene, Chlorine
Propylene Oxide
Cumene
Ethylene Oxide, Syngas
TPA, PDO
Pyrometallurgic SO2
Natural gas
P-Xylene, Methanol
BTX
Isobutanol
Caustic Soda
Hydrogen
Hydrochloric Acid*
Propylene, BTX, C4
Acetone
Based on renewable resources:
Hydrogen
Acetic Acid
Lactic Acid
Ethyl Lactate
Polylactides (PLA)
Succinic Acid
PHA
PDO
PTT
0.32, 3.2
31, 310
150
188
140
75
4
45.5
250
Potato Peels, Steam
Dextrose
Dextrose
Lactic Acid, Ethanol
Lactic Acid, Ethanol
Dextrose
Dextrose, Fatty Acids
Dextrose
PTT, TPA
9
3.1. Investment costs
Investment figures for petrochemical installations were obtained from SRI reports,8
where they are calculated by resizing plant designs to the design capacity. Figures
corresponding to large-capacity plants were adopted in all cases, with indicative typical
plant sizes shown in table 3.II. This choice was made in order to account for the
benefits of economies of scale for bulk chemicals.
Table 3.II: Typical plant sizes for bulk chemicals (industry sources)
Chemical Category
(relevant examples)
Specialty solvents
(Carbon Sulfide)
Specialty Polymers
(Polycarbonate)
Textile Polymers
(Polyamide)
Food Chemicals
(Citric Acid)
Bulk Solvents
(Acetone)
Bulk Polymers
(PET, PVC)
Bulk Intermediates
(Acetic Acid)
Bulk Chemicals
(Methanol, Olefins)
10-20
KT
20-50
KT
TYPICAL PLANT SIZES
50-100 100-200 200-400 400-700 7001200 KT
KT
KT
KT
KT
Total investment costs are the sum of two categories: “battery limits” and “offsites”. Battery limits are taken as the process equipment, including that for feed
treatment, product separation and purification, recycle handling and product packaging.
The off-sites portion of the investment figures covers refrigeration, utilities, waste
disposal and storage facilities for the specific process, including utility generating
facilities such as steam boilers, water-treating units, cooling towers. Electricity
generation installations, conversely, are not included in the off-sites investments (see
section 3.2). General service facilities, assumed to be 20% of the sum of battery limits
plus utilities and tankage, are also included in the off site investment costs. All
investment figures include 25% contingency.
8
SRI PEP YEARBOOK 2000, general methodology section.
10
In the production costs, investment costs were ultimately accounted for in a
different way than in the SRI pep reports. SRI includes 10% yearly depreciation,
allocated to the yearly production capacity, in the plant-gate costs, and a further 25%
return on investment to calculate the product value. This is equivalent to a total 35%
capital charge. This figure was found to produce minimum selling prices in many cases
in large excess of the market list prices, which in fact often entail significantly lower
profit margins. Based on Shell sources and expert opinion from Shell9, 20% capital
charge was used as a figure to calculate the profited cost. This figure was chosen as
representative to allow for comparing the profitability of different (petrochemical and
biotechnological) synthetic routes to the same product.
In principle, soaring energy prices can indirectly impact the investment costs
through, for instance, increased production cost of stainless steel and increased
manufacturing costs. A possible way to account for these effects is by means of an
economic input-output model.10 This method, through the inter-linkages between
economic sectors, tracks back the supply chain of the requirements needed to produce,
say, a steam boiler to include, for what concerns the present paper, not only the energy
inputs to final assembly, but also those in the mining and smelting of metals. However,
this kind of analysis was out of the scope of the present study and was hence not
performed; investment costs are therefore assumed as independent of energy prices.
3.2. Utilities costs
Except for electricity, capital charges are not included in utilities costs, since utility
installations are capitalised as part of the off-sites investment costs for the chemical
plants. The assumptions made to calculate utility costs are thus as follows:

Electricity cost is estimated as the electricity generation cost (including
depreciation of fixed capital), averaged across the energy mix in use in the EU
in the year 2000. Capital costs were accounted for by considering a 10%
discount rate over a depreciation period of 15 years,11 corresponding to 13.5%
annuitised capital charge. This method resulted in a baseline (year 2000) utility
cost of 5.45 US cents per kWh, matching with the reported EU mean electricity
9
Nisbet T. (Shell), private communication
Leontief, Wassily W., Input-Output Economics. 2nd ed., New York: Oxford University Press, 1986
11
In accordance with parameters reported by Framatome at the UNECE Roundtable Facilitating
Investment in the Electricity Sector in the Transition Economies, Geneva, 19. Nov. 2003
http://www.unece.org/ie/se/pp/elec/framatome.pdf
10
11
price to industry for the same year.12 Techno-economic data for electricity
generation (such as investment costs, conversion efficiencies, fixed and variable
costs) was derived from ECN data13 (see Appendix 2). The inputs to the
calculation of electric power price are oil, gas, coal and biomass prices 14 and
their relative shares in the total power generation in the EU at a certain time.
However, a series of approximations (discussed in Section 4.2) were made in
order to reduce the number of variables to only one (oil price or time, depending
on the type of analysis).

Cooling water cost is the cost of operating a cooling tower supplying
recirculating cooling water. This cost is mainly electricity use, hence it is
assumed to scale linearly with electricity cost.

Process water cost is typical for water that has been softened and filtered (but
not deionised). This cost is assumed to be related more to materials than to
energy use and, as an approximation, independent of energy price.

Inert gas, a CO-free mixture of carbon dioxide, nitrogen and rare gases, is
generated on-site. The cost of inert gas is assumed as independent of energy
price15.

Steam cost is estimated on the basis of burning fuel oil to raise 600 psig steam
superheated to 399C. Since fuel is the overwhelming part of the total variable
costs of steam generation, steam cost is assumed to scale linearly with oil price.
The base values of utility costs are, for consistency, a standard set utilised by the
BREWTOOL (see Appendix 2).
3.3. Production cost bases
Operating costs, as adopted by SRI, include the following fixed figures, estimated for
Germany and assumed as representative for Europe:

Operating labour: 39.07 US$/work hour, including costs of fringe benefits and
10% shift overlap.
“European Union Energy & Transport in Figures 2003”, European Commission, Directorate-General
for Energy and Transport, in co-operation with Eurostat:
http://europa.eu.int/comm/dgs/energy_transport/figures/pocketbook/doc/en_prices_2003.pdf
13
Smekens K.E.L, Lako P, Seebregts A.J, “Technologies and technology learning, contributions to IEA's
Energy Technology Perspectives”, ECN-C--03-046, August 2003
14
In principle, nuclear fuel should be included too. It is however assumed as constant (see appendix II)
15
In the cases covered by BREWPROFIT inert gas represent a minimal part of total costs; the errors
introduced by this approximation are hence hardly significant.
12
12

Operating supplies: 10% of operating labour.

Control laboratory: 20% of operating labour.

Plant overhead: 80% of total labour.

Taxes and insurance: 2%/year on total fixed capital.

Maintenance costs (labour plus materials): 1.5% to 6% of battery limits
investment depending on the specific process/type of installation, plus
1.5%/year of refrigeration facilities investment.

General and administrative, sales and research costs are lumped together and
taken as a percentage of the calculated profited cost, ranging from 3% for
chemicals used captively, over 5% for commodity chemicals, to 10% for
commodity plastics and intermediates between commodity and specialty
chemicals. Specialty chemicals are outside the scope of this project report.
Since General, Administrative, Sales and Research costs (GASR) are defined as
a percentage not of the costs calculated thus far (Upstream Figure, UF) but of
the final figure (FF), the calculation is performed as follows:
UF  GASR  FF
1)
by expressing as GASR% the fraction (0.03 to 0.10) of FF corresponding to the
GASR costs, eq. 1 is rearranged as:
UF  GASR%  FF  FF
2)
FF 
3)
UF
1  GASR%
3.4. Material costs
Material costs are treated differently depending on whether they are assumed as energydependent or energy-independent.

Energy-dependent material costs are typically the costs of chemical
intermediates, of acids and bases, of oxidisers (chlorine, oxygen). Each of these
is calculated in a separate sheet of the PET BREW Excel tool. It was assumed
13
that manufacturers purchase all these materials at a price equal to their profited
cost calculated with a 20% charge on all fixed capital.
Whenever techno-economic data on a specific material is missing, its
manufacturer-purchase price is expressed as the list or contract price for the
reference year and linked to the profited cost of a similar product for future
projections. This method was implemented, typically, to calculate the profited
cost of a material of different grade than the one for which complete technoeconomic documentation was available (such as chemical-grade propylene
instead of polymer-grade propylene), and to link the profited cost of
hydrochloric acid (missing data) to that of phosphoric acid. As an example,
according to the following definitions:
be HClMP the market price of hydrochloric acid in year 2000
be H3PO4MP the market price of phosphoric acid in year 2000
be H3PO4PC the calculated profited cost of phosphoric acid
the profited cost of hydrochloric acid is expressed as:
HCl PC  H 3PO4 PC
4)

HCl MP
H 3PO4 MP
Energy-independent material costs are, in the present model, the costs of non-oil
based commodities such as rock salt, sodium carbonate and phosphate rock and
the costs of high value materials (such as most catalysts, ion-exchange resins,
zeolites, etc.) used in small quantities for the production of base chemicals. All
these prices were taken from SRI estimates for average awarded contracts in
Germany in year 2000.
3.5. Cost allocation
Whenever a process yields multiple products, it is necessary to perform an allocation of
the total (profited) production costs, which are defined as the total (profited) costs of the
process. The standard economic approach is to relate the process to one main product,
while all the others are sold out and their value subtracted (as by-product credits) from
the material costs. This procedure is straightforward in a static analysis (in which the
values of all materials is known a priori based on market prices) but requires some
14
attention in the case of dynamic analysis (in which the value of materials is calculated
based on the current price of oil, gas, utilities).
The subtraction of by-product credits form material costs can be done whenever the
model contains an independent way of calculating them. This is always the case for
low-value products (such as fuel oil, pyrolysis gasoline, fuel gas), for very high value
(non base chemicals) products, whose value is assumed independent of oil and utility
prices, and whenever a separate excel sheet exists that calculates the profited cost of a
given product without cyclical cross-references.
In all other cases, when the value of a by-product is calculated by the same excel sheet,
the above approach is not possible due to the occurrence of cyclical reference. An
example of this case is the production of phenol and acetone by decomposition of
cumene (main synthetic route for both products): if one assumed that phenol is the main
product and acetone the by-product (or the contrary), it would be necessary to subtract
from the production cost of phenol a by-product credit (acetone) whose value is
unknown until the final calculation is completed. To overcome this problem, it was
assumed that the profited costs of products made by the same process maintain constant
ratio, and the allocation is done based on product value, using year 2000 values as a
reference.
This scheme can be implemented in several ways, the most straightforward of which is
probably to calculate a final figure for the overall profited cost of the process and then
allocate it to the different products on the basis of their reference economic value: if N
is the number of products over which the total profited cost PC is allocated, vi is the
market value of the ith product in the reference year 2000 and Qi the amount of it that is
produced, the profited cost of the ith product pci can be expressed as:
5)
pci  PC
vi
N
v Q
j 1
j
j
15
4. INPUT SCENARIOS FOR FUTURE COST PROJECTIONS
This chapter introduces the scenario data later utilised for cost projection of
chemicals from renewable resources vs. their petrochemical equivalents. Future trends
are investigated by integrating fossil fuel price projections published on account of the
Intergovernmental Panel on Climate Change. These data, although one should always
be cautious about the validity of any long-term forecast, are sufficient to project the cost
of petrochemical products. On the other hand, in order to project the cost of chemicals
form renewable resources it is necessary to know not only the price of fossil fuels
(which impact the overall costs through utility prices) but also of sugar, which is the
universal feedstock of fermentation processes and hence of the prospective bulk
biotech-chemical industry. Fermentable sugars can have essentially three origins:
 Sugar crop (sugar beet, sugar cane)
 Hydrolysis (saccharification) of starchy crop (maize, wheat)
 Chemical or enzymatic digestion of lignocellulose (agricultural waste such as corn
stover and straw, logging waste, energy crop)
According to previous assessments,16 the European bio-climatic specificity is such
that fermentable sugars from lignocellulosic origin are regarded as offering the best
economic prospects; therefore the sugar cost estimate incorporated in the model
assumes lignocellulosic origin. Such estimate is composed of two essential parts: an
experience function, since the technology to convert cellulose to fermentable sugar
cannot at the present stage be regarded as mature, and the price of biomass. As regards
the estimate of said experience curve, several studies from the US National Renewable
Energy Laboratory are available. On the other hand, due to the limited availability of
comprehensive literature on biomass price projections, a simple method, relying on the
combination of exogenous data from different sources, is proposed and applied to
estimate the cost of biomass as a feedstock for the European chemical industry in
relation to the projected biomass demand both as a chemical feedstock and as a fuel for
electricity generation.
16
Convened expert opinion at BREW roundtable, Utrecht, May 2004
16
4.1. SRES Scenarios:
In response to a 1994 evaluation of the earlier IPCC IS92 emissions scenarios, the 1996
Plenary of the IPCC requested the Special Report on Emissions Scenarios (SRES).
The SRES scenarios cover a wide range of the main driving forces of future emissions,
from demographic to technological and economic developments. None of the scenarios
in the set includes any future policies that explicitly address climate change (such as
carbon taxing), although all scenarios necessarily encompass various policies of other
types. Since 1998, when the preliminary scenarios were made available to climate
modellers, SRES scenarios have been widely used as a basis for the assessment of
climatic changes. These scenarios are derived from an initial choice of four scenario
families (storylines) characterised by future development paths different by:
o Nature of the global and regional demographic developments in relation to other
characteristics of the storyline.
o Extent to which economic globalisation and increased social and cultural
interactions continue over the 21st century.
o Rates of global and regional economic developments and trade patterns in relation
to the other characteristics of the storyline.
o Rates and direction of global and regional technological change, especially in
relation to the economic development prospects.
o Extent to which local and regional environmental concerns shape the direction of
future development and environmental controls.
o Degree to which human and natural resources are mobilized globally and regionally
to achieve multiple development objectives of each storyline.
o Balance of economic, social, technological, or environmental objectives in the
choices made by consumers, governments, enterprises, and other stakeholders.
Figure 1 shows the positioning of the four scenario families (A1, A2, B1, B2) in the
characteristic two-dimensional space global/regional, economic/environmental.
17
Figure1: The four SRES scenario families. Source: SRES (IPCC)
 The A1 storyline and scenario family describes a future world of very rapid
economic growth, global population that peaks in mid-century and declines thereafter,
and the rapid introduction of new and more efficient technologies. Major underlying
themes are convergence among regions, capacity building, and increased cultural and
social interactions, with a substantial reduction in regional differences in per capita
income. The A1 scenario family develops into three groups that describe alternative
directions of technological change in the energy system. The three A1 groups are
distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy
sources (A1T), or a balance across all sources (A1B).
 The A2 storyline and scenario family describes a very heterogeneous world. The
underlying theme is self-reliance and preservation of local identities. Fertility patterns
across regions converge very slowly, which results in continuously increasing global
population. Economic development is primarily regionally oriented and per capita
economic growth and technological change are more fragmented and slower than in
other storylines.
 The B1 storyline and scenario family describes a convergent world with the
same global population that peaks in mid-century and declines thereafter, as in the A1
18
storyline, but with rapid changes in economic structures toward a service and
information economy, with reductions in material intensity, and the introduction of
clean and resource-efficient technologies. The emphasis is on global solutions to
economic, social, and environmental sustainability, including improved equity, but
without additional climate initiatives.
 The B2 storyline and scenario family describes a world in which the emphasis is
on local solutions to economic, social, and environmental sustainability. It is a world
with continuously increasing global population at a rate lower than A2, intermediate
levels of economic development, and less rapid and more diverse technological change
than in the B1 and A1 storylines. While the scenario is also oriented toward
environmental protection and social equity, it focuses on local and regional levels.
4.2. Four Scenarios for Fossil Fuel Prices:
The SRES contains separate oil, gas and coal price projections until year 2100 for
the following scenarios: A1B, A1C, A1G, A1T, B1, B1G, B1T, A2, B2.17 In order to
restrict the number of scenarios to a more manageable number, and for consistency with
other data sources that mainly focussed on the four principal storylines, the four
datasets of the A1 family and the three of the B1 family were averaged out as A1 and
B1, yielding the following price projections for the three energy carriers (data is only
presented until year 2050 for consistency with the overall demarcation of the BREW
project):
Table 3.I: projected fossil fuel price increases (2000-2050) in four scenarios.
Scenario
Year
2000
2010
2020
2030
2040
2050
Oil
1
1.69
1.54
1.53
1.61
1.87
A1
Gas Coal
1
1
1.19 1.24
1.29 1.23
1.74 1.29
1.73 1.24
2.12 1.43
Oil
1
1.49
1.88
2.16
2.78
3.31
A2
Gas Coal
1
1
1.00 1.00
1.38 1.01
1.79 1.32
2.07 1.48
2.40 1.54
Oil
1
1.58
1.70
1.58
1.71
1.50
B1
Gas Coal
1
1
1.11 1.26
1.27 1.46
1.28 1.32
1.38 1.27
1.28 1.02
Oil
1
1.42
1.51
1.81
2.16
2.64
B2
Gas Coal
1
1
0.97 1.10
1.19 1.76
1.35 1.32
1.59 1.32
1.80 1.38
Table 3.I shows price indices, i.e. multiplicative factors to the base (year 2000)
value. By using these factors as three input variables to integrate SRES scenarios in the
BREWprofit model (see chapter 3), the results of the model calculations can either be
17
Scenarios A1C, A1G and B1G: The transition away from conventional oil and gas either leads to a
massive development of unconventional oil and gas resources (A1G, B1G) or to a large-scale synfuel
economy based on coal (A1C).
19
reported in a single XY product-cost vs. time plot, or, to visualise explicitly the effects
of energy price increases, in a space defined by three independent variables: oil, gas,
and coal prices. This is a four-dimensional space (three energy variable plus product
cost), which could in principle be simply plotted by sections (cost vs. oil price, with gas
and coal prices constant, and the other two permutations). However, the resulting
graphs would not represent probable trajectories.18
A more useful way of presenting energy-carrier dependency of the profited cost of a
chemical is the following: since the most influential variable for petrochemical products
is oil price, the number of independent variables can be reduced to one by linear
regression of the gas and coal price indices (iG and iC) to the oil price index (iO). Figure2
shows a sample linear regression (scenario A2), in which the fitting function (the offset
by -1 is in order to fit only the variations of the index from the base value 1) is:
iG  1  k G (iO  1)

iC  1  k C (iO  1)
6)
4.5
Gas Coefficient=0.73
Coal Coefficient=0.33
Gas and Coal Price Indices
4.0
3.5
3.0
2.5
2.0
1.5
1.0
1
2
3
4
5
6
Oil Price Index
Fig. 2: Gas and Coal price indices fitted to the Oil price index. Scenario A2.
18
It is unlikely that, at any point in time, only one of the three fossil energy carriers will soar in price
while the other two remain constant.
20
The coefficients (k’s in equation 6, slopes of the dashed lines in fig.2) resulting from
linear regression for the four scenarios are reported in table 3.II.
Table 3.II: Linear regression coefficients linking coal and gas prices to oil price, in four scenarios.
Scenario
Gas Coeff
Coal Coeff
A1
1.1
0.8
A2
0.73
0.33
B1
0.71
0.71
B2
0.59
0.49
4.3. Scenario-related Biomass Use and Price
Section 4.2 reported on the assumptions made to obtain oil and other fossil energy
input prices for BREWprofit. The present section describes the method used to obtain
the biomass input price necessary to perform comparative cost analysis between fossilbased and renewable-based bulk chemicals.19 This method relies on the combination of
exogenous data from different sources, and is not meant to provide quantitative figures
but indicative estimates; the method, starting from the main underlying assumptions, is
worked out as follows:
All lignocellulosic biomass available in Europe is considered as a single pool,
consisting of woody energy crop, agricultural waste, logging waste. Cost-supply curves
for energy crop were reported by Hoogwijk et al;20 table 4.I shows the amount of
lignocellulosic biomass available within a certain price range for Western and Eastern
Europe cumulated (NIS excluded), for year 2050:
Table 4.I: Energy-crop biomass available in Europe, sorted by price ranges, in year 2050.
Scenario
A1
A2
B1
B2
Price range 1-2$/GJ 2-4$/GJ >4$/GJ 1-2$/GJ 2-4$/GJ >4$/GJ 1-2$/GJ 2-4$/GJ >4$/GJ 1-2$/GJ 2-4$/GJ >4$/GJ
EJ
10
11
2
12
6
4
11
6
0
15
8
3
These figures do not take into account logging and agricultural waste. This
component was included in the present model by assuming a cost between 1 and 2
$/GJ21 and then adding the relative amount to the pool available at the cheapest supply
19
This section is also worked out in order to assess if the development of bulk chemistry based on
renewable resources will result in strong competition for biomass use with the energy sector, possibly
causing biomass price to increase.
20
Hoogwijk M, Faaij A, de Vries B, Turkenburg W. “Potential of grown biomass for energy under four
GHG emission scenarios: Part B: exploration of the regional cost-supply curve (2004, to be submitted)”.
Included in M.Hoogwijk’s PhD thesis.
21
Ruth, M.F, Wooley, R.J, “The cost of lignocellulosic sugar for commodity chemical production”,
National Renewable Energy Laboratory, http://afdcweb.nrel.gov/pdfs/4913.pdf
21
price in table 4.I. For the same geographical region, the available amount of logging and
agricultural waste was estimated by Smeets et al.22 as varying between 4 and 6 GJ per
year, depending on scenario parameters. The three scenarios exploreded by Smeets et
al., essentially differing by land productivity, are not related to the SRES scenarios. For
this reason, and since the inter-scenario difference is limited, we decided to use an
average figure (5EJ per year) for all SRES scenarios. By combining this figure with
table 4.I, and by assuming average biomass prices of 1.5, 3 and 5 US$/GJ for biomass
available at the price ranges of table 4.I (1-2, 2-4 and above 4 US$/GJ), we obtain:
Table 4.II. Lignocellulosic biomass available in Europe, sorted by price, in year 2050.
Scenario
A1
Price range 1.5$/GJ 3$/GJ
En.Crop (EJ) 10
11
Waste (EJ)
5
0
TOTAL (EJ)
15
11
A2
5$/GJ 1.5$/GJ 3$/GJ
2
12
6
0
5
0
2
17
6
B1
5$/GJ 1.5$/GJ 3$/GJ
4
11
6
0
5
0
4
16
6
B2
5$/GJ 1.5$/GJ 3$/GJ
0
15
8
0
5
0
0
20
8
5$/GJ
3
0
3
As a first approximation, the amount of biomass available is considered constant in
time, equal to the level predicted for year 2050. This assumption is not expected to
introduce large errors when calculating biomass price (see below) since biomass
demand in early years is low.
It is then assumed that, at any point in time, there will be competition for this
limited pool of biomass between the chemical industry (bio-feedstock) and power
generation. Biomass will be available at the same average conditions (price) for both
destinations. Biomass will be available at the lowest price (1.5 US$/GJ) up to
exhaustion of the pool available at that price. At this point the pool available at 3
US$/GJ will be tapped into, and so on. The average biomass price in relation to
demand, for the four scenarios, is shown in table 4.III (note that the total geographical
potential is different under the four scenarios).
22
E. Smeets, A. Faaij and I. Lewandowski, confidential report (Global Quickscan)
22
Table 4.III: Demand-related biomass price. Rows 2-9 not shown (constant value 1.50$/GJ)
Demand
A1
A2
B1
B2
EJ/year Price, $/GJ Price, $/GJ Price, $/GJ Price, $/GJ
1
1.50
1.50
1.50
1.50
11
1.50
1.50
1.50
1.50
12
1.50
1.50
1.50
1.50
13
1.50
1.50
1.50
1.50
14
1.50
1.50
1.50
1.50
15
1.50
1.50
1.50
1.50
16
1.59
1.50
1.50
1.50
17
1.68
1.50
1.59
1.50
18
1.75
1.58
1.67
1.50
19
1.82
1.66
1.74
1.50
20
1.88
1.73
1.80
1.50
21
1.93
1.79
1.86
1.57
22
1.98
1.84
1.91
1.64
23
2.02
1.89
1.70
24
2.06
2.02
1.75
25
2.10
2.14
1.80
26
2.13
2.25
1.85
27
2.24
2.35
1.89
28
2.34
1.93
29
2.03
30
2.13
31
2.23
Biomass demand for power generation is taken as from IPCC projections.23 The
IPCC scenarios adopted by this source are an earlier version than the SRES ones, hence
B1 and B2 are merged in a single scenario, and additional scenarios (A3, C) are
discarded. The cumulative figures for Western and Eastern Europe (excluding NIS) are:
Table 4.IV: Biomass demand for electricity generation, Europe 2000-2050 (Source: WEP)
Year
2000
2010
2020
2030
2040
2050
A1
A2
B1
B2
Biomass for Power generation, EJ/year
2.12
2.57
1.95
1.95
1.73
1.92
1.73
1.73
1.76
3.66
1.79
1.79
2.08
6.28
1.8
1.8
3.735
12.75
4.095
4.095
5.39
19.22
6.39
6.39
Biomass demand as chemical feedstock is estimated by combining the predicted
growth of the European chemical industry (in terms of total mass produced) with an
estimate for the renewable/petrochemical substitution rate. The renewable-based
Nakicenovic N, Grubler A, McDonald A, “Global Energy Perspectives”, Cambridge University Press
ISBN 0-521-64569-7
23
23
production volume obtained in this way is translated into biomass demand through
average energy-content conservation relationships available in the literature.24 The
assumptions made to obtain the biomass demand as chemical feedstock are the
following, and the results are summarised in table 4.V:

The total production of bulk plastics in the European Union (EU15) in year 2000
was quantified as 45 Megatons,25 and estimated to reach 57 MT in 2010 and 70 MT in
2020 (base case). These values were extrapolated up to 2050 by comparison with other
sources containing longer-term projections.26,27 Increasing those values by 20% yielded
the figures assumed representative of the enlarged EU (EU25).28 In order to also include
all chemicals other than polymers within the scope of the BREW project (solvents,
lubricants, surfactants, large volume intermediates) the values for plastics were doubled.

The substitution rate of renewable for fossil feedstock (rapid penetration case),
in accordance with the figures published by the US DOE29 for the “technology roadmap
for plant/crop-based renewable resources”30 was assumed to be 10% in 2020 (fivefold
increase of the current 4%)31 and 50% in 2050 (another fivefold increase). Combined
with the projected growth of the European chemical industry, this results in an
increased production of bulk chemicals based on renewable resources from 2.16 MT in
year 2000 to 120 MT in 2050.
Bender M. H, “Potential conservation of biomass in the production of synthetic organics” Resources,
Conservation and Recycling 30 (2000) 49–58
25
Phylipsen D, Kerssemeeckers M, Blok K, Patel M, de Beer J., “Clean Technologies in the Material
Sector”, Current and Future Environmental Performance of Material Technologies, Ecofys E 9087, 2002
26
Crank, M.; Patel, M.; Marscheider-Weidemann, F.; Schleich, J.; Hüsing, B.; Angerer, G.: Technoeconomic Feasibility of Large-scale Production of Bio-based Polymers in Europe (PRO-BIP). Report
prepared for the European Commission’s Institute for Prospective Technological Studies (IPTS), Sevilla,
Spain. Prepared by the Department of Science, Technology and Society/Copernicus Institute at Utrecht
University, Utrecht, Netherlands and the Fraunhofer Institute for Systems and Innovation Research,
Karlsruhe, Germany, 2004
27
VLEEM, “Very Long Term Energy Environment Modelling”, EC/DG Research Contract ENG2-CT2000-00441
28
Sources: CEFIC, http://www.cefic.org/factsandfigures/downloads/allgraphs/F&F-June-2004-web.ppt,
and OECD, “OECD Environmental Outlook for the Chemicals Industry”, 2001, http://www.oecd.org/ehs.
29
Several data sources utilised in this section and in chapter 5 refer to US data. This is because more
comprehensive studies have been made for biomass to sugar conversion in the US than in Europe. It is
assumed that the same parameters are indicatively applicable to the European case also.
30
U.S. Department of Energy (DOE): The technology roadmap for plant/crop-based renewable resources
2020 – Research priorities for fulfilling a vision to enhance U.S. economic security through renewable
plant/crop-based resource use. DOE/GO-10099-706, Washington, 1999
31
For Europe, the 1998 figure was estimated as 3.1%. Source: ECCP (European Climate Change
Programme) – Long report. [http://europa.eu.int/comm/environment/climat/eccp_longreport_0106.pdf]
Brussels, 2001
24
24

The average energy content of bulk chemicals was assumed, as estimated by
Patel32, as 34GJ/T. In terms of energy content, therefore, the increase is from 73PJ in
2000 to 4.1 EJ in 2050.

(Bio)mass conservation in the production of chemicals: since carbohydrate-
based feedstock is highly oxygenated, the reduction to olefin-type building blocks
implies a significant loss of mass. In terms of energy, depending on the type of
chemicals produced, the use of bio-feedstock implies an increase of total feedstock
requirements up to 1/3 compared to using fossil feedstock. Based on these
considerations and on the average composition of bulk chemicals, 1.57 GJ of biomass
are required to produce 1 GJ of bulk chemicals.33 Biomass demand for the chemical
industry increases from 115 PJ in 2000 to 6.4 EJ in 2050.
Table 4.V: Biomass demand for the chemical industry, Europe 2000-2050
Year
2000
2010
2020
2030
2040
2050

Polymers Production (MT)
All Bulk
EU15
EU25
Chemicals (MT)
45
54
108
57
68.4
136.8
70
84
168
81
97.2
194.4
91
109.2
218.4
100
120
240
From Renewables
Feedstock
%
MT
EJ
EJ
2
2.16
0.07
0.12
5
6.84
0.23
0.37
10
16.8
0.57
0.90
20
38.88
1.32
2.08
35
76.44
2.60
4.08
50
120
4.08
6.41
By combining the data of Tables 4.IV and 4.V one obtains the estimate for total
lignocellulosic biomass demand in the enlarged European Union up to 2050.34 Table
4.V can then be used to obtain the average price of biomass meeting that demand. These
results are summarised in table 4.VI.
Patel, M.: “Life cycle assessment (LCA) results for conventional and bio-based plastics”. Lecture held
at the Department of Polymer Chemistry at the Center of Molecular and Macromolecular Studies, Polish
Academy of Sciences, 19 February 2003, Lodz, Poland
33
Bender M. H, “Potential conservation of biomass in the production of synthetic organics” Resources,
Conservation and Recycling 30 (2000) 49–58
34
This estimate does not include biomass for biofuels. The EU has set targets for the minimum
proportion of biofuels to be sold on the member states’ markets at 2% In 2005 and 5.75% in 2010. The
current energy consumption of EU-25’s transportation sector is of the order of 16 EJ and projected,
depending on the scenario, to reach between 18 and 30 EJ in 2050 (ref: WEP), 5.75% of which is
between 1 and 1.7 EJ. This requirement will be fulfilled by different types of biofuels. Ethanol can be
blended with gasoline, while biodiesel is mostly composed of rapeseed oil. We assume that land use for
rapeseed crop does not compete with energy crop and that only a fraction of the ethanol requirement will
be produced from lignocellulosic feedstock. Under these assumptions, biofuel production does not
compete significantly with power generation and chemicals production for lignocellulosic feedstock.
32
25
Table 4.VI: Total biomass demand and price, Europe 2000-2050
Scenario
Year
2000
2010
2020
2030
2040
2050
A1
Total Biomass
EJ/year
$/GJ
2.24
1.5
2.10
1.5
2.66
1.5
4.16
1.5
7.82
1.5
11.80
1.5
A2
Total Biomass
EJ/year
$/GJ
2.69
1.5
2.29
1.5
4.56
1.5
8.36
1.5
16.83
1.5
25.63
2.2
B1
Total Biomass
EJ/year
$/GJ
2.07
1.5
2.10
1.5
2.69
1.5
3.88
1.5
8.18
1.5
12.80
1.5
B2
Total Biomass
EJ/year
$/GJ
2.07
1.5
2.10
1.5
2.69
1.5
3.88
1.5
8.18
1.5
12.80
1.5
The conclusion that can be drawn from table 4.VI is that, under the present
assumptions and the four considered scenarios, biomass demand in the EU until 2050 is
not expected to reach levels as high as to affect biomass price. The only exception is
year 2050 in scenario A2 (2.2 US$/GJ). Since this is the only point of deviation from
the base value of 1.5 US$/GJ in a rough analysis that should only be used to derive
indicative values, and since the assumed penetration rate of renewable feedstock for
bulk chemicals is by all means optimistic, in the following of this report biomass price
will be considered constant, equal to 1.5 US$/GJ.
26
5. COMPARATIVE COST ANALYSIS: BULK CHEMICALS FROM FOSSIL vs.
RENEWABLE RESOURCES. PRELIMINARY RESULTS
This chapter is concerned with comparative cost analysis of selected bulk chemicals
from renewable resources vs. their petrochemical equivalents. Parameters are changed,
starting from those describing the current situation, in accordance with the scenario data
introduced in Chapter 4. This analysis should be understood as preliminary, since the
final parameters relative to bio-based chemicals were not available at the time when this
report was written.
In the following sections, three case studies are presented, concerned with
replacements based on renewable resource for bulk solvents, plastics and raw materials
derived from natural gas. The results are presented in two different formats: as Break
Even Oil to Sugar Price (BEOSP) charts, indicating the values of oil and sugar price at
which a certain product based on renewable resources becomes competitive with its
petrochemical analogue, and as time-domain projections. Such projections are presented
for the four scenarios introduced in chapter 4; as reading key, scenarios A1 And B1
represent conditions of relatively low long-term fossil fuel prices, whereas under
scenarios A2 and B2 fossil fuels experience a steep increase in price towards the midst
of the century.35
Sugar price is assumed to be equal to the market price of dextrose (187 US$/ton) in
year 200036 and to decrease in the future according to NREL projections.37 On the basis
of prospected improvement of enzymatic productivity and of feedstock collection
structure for the lignocellulose-to-fermentable-sugar conversion, NREL projections are
as follows:
Table 5.I: Prospected fermentable sugar price reduction
Current
Minimum Sugar
Selling Price
(US$/ton)
187
Potential
Year 2010 Large
Near Term Year 2005 Year 2010 Capacity
147
115
105
71
35
At the time this report was written, a conjuncture of geopolitical circumstances had caused oil price to
soar to more than 40 US$/Barrel, a level largely in excess of SRES projections. This figure was assumed
as anomalous and not representative of long-term averages.
36
Standard data for BREW (BREWtool)
37
Ruth, M.F, Wooley, R.J, “The cost of lignocellulosic sugar for commodity chemical production”,
National Renewable Energy Laboratory, http://afdcweb.nrel.gov/pdfs/4913.pdf
27
It is interesting to note that the prospected final selling price of 71 US$/ton for large
capacity (of the order of 1 Megaton per year) is very close to the current selling price of
sugar from sugar cane in Brazil (68-72US$/ton).38
For the purpose of scenario-integration of sugar price projections, in the following
of this chapter it will be assumed that the representative sugar transfer price be 187
US$/ton in year 2000, 105 US$/ton in year 2010 and 71 US$/ton from year 2020
onwards. These figures are derived from projections made for the US. It is here
assumed that they can also be applicable to the European case, mainly due to the lack of
comparably comprehensive studies referred to Europe.
BEOSP charts are presented as a function of oil price and, for clarity of reading, at
the highest (year 2000, 187 US$/ton) and lowest (from year 2020 onwards, 71 US$/ton)
sugar prices considered. In order to be able to plot profited costs against only one
variable (oil price), BEOSP charts have been generated under the approximation of
linking gas and coal prices to oil price through fitting of SRES projections (see Chapter
3, Section 4.2 and Appendix II). Regarding said fitting, all BEOSP charts shown in this
chapter have been made with the parameters (see Table 3.II) relative to scenario B1.39
This approximation was only used for BEOSP charts, whereas in time-domain cost
projection charts all 3 parameters are input as from SRES projections.
5.1 Solvents
Disregarding ethanol, already produced in bulk quantity from fermentation at
significantly lower cost than from hydration of petrochemical ethylene, for the BREW
project ethyl lactate was selected as the solvent based on renewable feedstock with the
best prospects to become competitive, in the short-to-mid term, with petrochemical bulk
solvents.
Ethyl lactate is produced by esterification of lactic acid with ethyl alcohol, and the
availability of low-cost bulk lactic acid can be considered key to the development of
ethyl lactate as a bulk solvent. Depending on achieved reduction of production costs,
ethyl lactate has been indicated as a suitable replacement for specialty solvents such as
38
Industry sources: C&T Brasil 2001, ASSOCANA 2001.
Using the set of parameters of a different scenario only causes minor differences in BEOSP charts,
since in most cases the profited cost gradient has a single steepest component (oil or natural gas
depending on the which is the main feedstock); in other words, the dependence on the most influential
variable is always reproduced exactly, while the approximation only interests variables on which the
product cost has a relatively weak dependence.
39
28
glycol ethers, semi-bulk solvents such as methyl-ethyl-ketone or bulk solvents such as
acetone and chlorinated solvents. Recent advancements in biotechnology have made
possible the production of bulk lactic acid at a minimum selling price of the order of
650 US$/ton (BREWprofit calculation), which allows the production of bulk ethyl
lactate at a cost already lower than the cost of glycol ethers and competitive with
methyl-ethyl ketone.
Figure 3 is the BEOSP chart for ethyl lactate (EL) compared to ethylene-glycolbutyl-ether (EGBE, taken as representative of glycol ethers), methyl-ethyl-ketone
(MEK) and acetone. The dash-lined arrows indicate the reduction of ethyl lactate
profited-cost induced by a 62% fermentable sugar cost reduction (assumed viable by
year 2020); the thick-lined arrows indicate three break-even-oil-prices (BEOP): B1
(17US$/barrel) is the BEOP for MEK and EL at current sugar price, B3 (49US$/barrel)
is the BEOP for acetone and EL at current sugar price and B2 (34US$/barrel) is the
BEOP for acetone and EL at year-2020 sugar price.
Profited Cost (USc/Kg)
100
EGBE
MEK
Acetone
Bio-EL-2000
Bio-EL-2020
80
60
40
B1
20
B2
30
B3
40
50
60
70
Oil Price (US$/Barrel)
Figure3: BEOSP chart for ethyl lactate (EL) compared to EGBE, MEK and acetone
29
Figure 3 shows that ethyl lactate could already be produced at lower cost than MEK
and glycol ethers, and that it can become competitive with bulk solvents such as acetone
under the condition of high oil price40. Figure 4 shows the ensuing comparative
profited-cost projection for EL and acetone, in relation to the four scenarios. The graph
shows that EL could become fully competitive with acetone between 2010 and 2020;
after 2020 the profited costs of the two solvents stabilise on relatively constant and
equal levels in scenarios A1 and B1, whereas in high long-term oil price scenarios (A2
and B2), ethyl lactate could be produced at a definitely lower cost than acetone.
AcO-A2
Profited Cost (USc/Kg)
100
AcO-B2
80
EL-A2
AcO-A1
EL-B2
EL-A1
AcO-B1
EL-B1
60
2000
2010
2020
2030
2040
2050
Year
Figure4: Profited-cost projection for ethyl lactate (EL) and acetone (AcO) in four scenarios
Table 5.II is a summary of the relevant parameters, relative to year 2000, used to
calculate the profited cost of the solvents presented in this section.
In agreement with the assessment diffused by Argonne National Laboratory, “Ethyl lactate, a low-cost,
environmentally friendly solvent”. http://www.ipd.anl.gov/biotech/programs/chemicals/ethyl-lactate.html
40
30
Overhead and taxes
(Usc/Kg)
Total Material costs
(Usc/Kg)
Dextrose (Usc/Kg of
feedstock)
Ethanol (Usc/Kg of
feedstock)
5.85
1.6
2.4
37.5
18.7
40
Acetone
180
525
5.5
2.35
2.25
45
MEK
60
888
10.5
4.4
4.1
36.3
EGBE
90
575
3.3
2.9
2.9
76.8
Ethylene Oxide
(Usc/Kg of feedstock)
Labour and materials
(Usc/Kg)
805
n-Butanol (Usc/Kg of
feedstock)
Utility costs (Usc/Kg)
188
MTBE raffinate
(Usc/Kg of feedstock)
Investment costs
(US$/ton capacity)
Ethyl
Lactate
Cumene (Usc/Kg of
feedstock)
Plant capacity
(KT/year)
Table 5.II: Relevant parameters for calculating profited cost of solvents (year 2000)41
60.9
96.5
53
36
5.2 Polymers
Two polymers based on renewable feedstock are examined in this sections:
polylactides (PLA), obtained by polymerisation of lactic acid, and Poly(Trimethylene
Terephthalate) (PTT), obtained by transesterification of terephthalic acid with 1,3PropaneDiol (PDO). One important difference between PLA and PTT is that, while
PLA can be obtained entirely from renewable feedstock (fermentation of sugar to lactic
acid), PTT requires petrochemical feedstock for the production of terephthalic acid,
whereas PDO can be either produced petrochemically or by fermentation.
In accordance with technical specifications and production-cost abatement
prospects, PLA is examined as a replacement for polyethylene (PE) and poly(ethyleneterephthalate) (PET), and renewable-resource based PTT is examined as a replacement
for PET and PTT fully based on petrochemical feedstock.
5.2.1 PTT
Figure 5 is the BEOSP chart for biotechnological PTT (bio-PTT) compared to PET
and petrochemical PTT (pet-PTT). The dashed arrows indicate the reduction of bio-PTT
profited-cost induced by the 62% year-2020 fermentable sugar cost reduction; the solid
arrows indicate three BEOP’s: B1 (29.5US$/barrel) is the BEOP for bio-PTT and pet41
Petrochemical data: SRI Yearbook 2000, Biotech data: confidential industry sources for the BREW
project (BREWtool)
31
PTT at current sugar price, B3 (74US$/barrel) is the BEOP for PET and bio-PTT at
current sugar price and B2 (41US$/barrel) is the same at year-2020 sugar price.
Profited Cost (USc/Kg)
200
PET
PTT
Bio-PTT-2000
Bio-PTT-2020
180
160
140
120
B1
20
B2
40
B3
60
80
Oil Price (US$/Barrel)
Figure5: BEOSP chart for bio-PTT compared to petrochemical PET and PTT
Figure 5 shows that bio-PTT can already be competitive with PTT fully based on
petrochemical feedstock, and that high oil prices combined with a significant reduction
of sugar price could make it even competitive with PET. Figures 6 and 7 show the
ensuing comparative profited-cost projections for bio-PTT and, respectively, pet-PTT
and PET, in relation to the four scenarios.
The economics of PTT production from renewable and petrochemical feedstock
(Fig.6) are already in year 2000 very similar; in the projections, soaring oil price
coupled to sugar price reductions makes the renewable-resource based product
increasingly cheaper than the petrochemical one, up to a profited cost difference in year
2050 varying between 15 and 30 US cents per Kilogram, depending on the scenario.
32
Pet-A2
Profited Cost (USc/Kg)
PTT
Pet-B2
Bio-A2
200
Bio-B2
Pet-A1
Bio-A1
Pet-B1
150
Bio-B1
2000
2010
2020
2030
2040
2050
Year
Figure6: Profited-cost projection for petrochemical (pet) and bio-PTT in four scenarios
PET-A2
Bio-PTT vs. Pet-PET
PTT-B2
Profited Cost (USc/Kg)
200
PET-B2
PTT-A2
PET-A1
PTT-A1
150
2000
PTT-B1
PET-B1
2010
2020
2030
2040
2050
Year
Figure7: Profited-cost projection for PET and bio-PTT in four scenarios
33
Conversely, PET is currently assessed as ca. 15 US cents per Kg cheaper than PTT
(Fig.7); soaring oil price and sugar price reduction could make bio-PTT fully
competitive with PET between 2010 and 2020. After 2020 the profited costs of the two
polymers stabilise on relatively constant and equal levels in scenarios A1 and B1,
whereas in high long-term oil price scenarios (A2 and B2), bio-PTT could be produced
at a definitely lower cost than PET.
5.2.2 PLA
Figure 8 is the BEOSP chart for PLA compared to Polyethylene and PET. The dashlined arrows indicate the reduction of PLA profited-cost induced by the 62% year-2020
fermentable sugar cost reduction; the thick-lined arrows indicate four BEOP’s: B2
(42US$/barrel) is the BEOP for PET and PLA at current sugar price, B1 (21US$/barrel)
is the BEOP for PET and PLA at year-2020 sugar price, B4 (76US$/barrel) is the BEOP
for PE and PLA at current sugar price and B3 (53US$/barrel) is the BEOP for PE and
PLA at year-2020 sugar price. It is interesting to note that the slope difference of the
profited cost vs. oil price graphs of petrochemical polymers and PLA is much more
pronounced than in the case of bio-PTT. This is due to the residual petrochemical
feedstock content (terephthalic acid) of bio-PTT.
180
Profited Cost (USc/Kg)
160
140
Polyethylene
PET
Bio-PLA-2000
Bio-PLA-2020
120
100
B2
B1
80
20
40
B3
60
B4
80
Oil Price (US$/Barrel)
Figure8: BEOSP chart for PLA compared to PE and PET
34
Figure 8 puts in evidence the opportunity offered by sugar price reductions for PLA
to become competitive with PET even at moderate oil price, whereas PLA could only
compete with PE in case of high oil price. Accordingly, in the scenario-related profitedcost projections of figure 9, PLA is produced at lower costs than PET starting from
2010, with a profited cost difference reaching more than 50 US cents per Kg by year
2050 in high oil-price scenarios.
Profited Cost (USc/Kg)
PET-A2
200
PET-B2
PET-A1
PLA-A2
PLA-B2
PET-B1
PLA-A1
PLA-B1
150
2000
2010
2020
2030
2040
2050
Year
Figure9: Profited-cost projection for PET and PLA in four scenarios
PE is currently assessed as more than 50 US cents per Kg cheaper than PLA (Fig.7),
a gap too large to be closed by sugar cost reductions alone. Only under high oil price
scenarios (A2, B2), and only after years 2030-2040, PE is projected to become costly
enough for PLA to become competitive.
35
Profited Cost (USc/Kg)
PE-A2
PE-B2
PLA-A2
PLA-B2
PLA-A1
PLA-B1
PE-A1
150
PE-B1
100
2000
2010
2020
2030
2040
2050
Year
Figure10: Profited-cost projection for PE and PLA in four scenarios
Table 5.III is a summary of the relevant parameters, relative to year 2000, used to
calculate the profited cost of the polymers presented in this section.
Labour and materials
(Usc/Kg)
Overhead and taxes
(Usc/Kg)
Total Material costs
(Usc/Kg)
Lactic Acid (Usc/Kg of
feedstock)
PDO (Usc/Kg of
feedstock)
TPA (Usc/Kg of
feedstock)
730
3.1
3.5
3.5
104
65
Bio-PTT
250
580
1.1
3.35
3.25
108
120
77
Pet-PTT
250
580
1.1
3.35
3.25
106
117.5
77
PET
180
762
2.8
3.45
3.25
94
PE
270
486
2.25
2.75
2.5
65
77
72.5
Ethylene (Usc/Kg of
feedstock)
Utility costs (Usc/Kg)
140
DMT (Usc/Kg of
feedstock)
Investment costs
(US$/ton capacity)
PLA
Ethylene Glycol
(Usc/Kg of feedstock)
Plant capacity
(KT/year)
Table 5.III: Relevant parameters for calculating profited cost of polymers (year 2000) 42
62
42
Petrochemical data: SRI Yearbook 2000, Biotech data: confidential industry sources for the BREW
project (BREWtool)
36
5.3 Materials based on Natural Gas
Sections 5.1 and 5.2 dealt with the replacement of bulk chemicals based on oil
(naphtha) as a feedstock; the present section is concerned with assessing the economic
prospects of producing two bulk commodity chemicals, acetic acid and hydrogen, from
renewable feedstock compared to their traditional synthetic route based on natural gas.
Techno-economic data for the production of these two chemicals from renewable
resources was available, at the time this report was written, only for relatively smallcapacity plants (30 KT for acetic acid and 0.3 KT for hydrogen, compared to current
large-capacity petrochemical plants of 500 and 6 KT, respectively). In order to compare
cost for the two synthetic routes, bulk-production plant parameters were obtained by
scaling the available ones.
Bulk-case parameters were generated by assuming plant capacities 10 times larger
than those of the original data (300 KT for acetic acid, 3 KT for hydrogen) and an
undifferentiated scale exponent of 0.7 for all fixed costs, including fixed capital charge,
labour, supplies, overhead and taxes: C1 and C2 are the original and rescaled plant
capacities expressed in tons per annum, F1 and F2 are the fixed costs of the original and
the rescaled plant and se is the scale exponent (0.7), the fixed costs per ton are then
reduced by 50%, as shown in equation 7.
7)
F2
F1
C2
C1
 C2 
 
C
 1
se
C2
C1
 10C1 


C1 


0.7
10C1
C1
5
10
1
2
In addition to the plant up-scaling, it is assumed that 30% reduction of the cost of
fermentation nutrients and microorganisms will be achieved by 2010 and 60% in 2020.
Since the fossil feedstock for the chemicals examined in this section is natural gas,
Break-even charts are plotted against natural gas price instead of oil price (break-evengas-to-sugar-price: BEGSP).
5.3.1 Acetic Acid
Despite the fact that the production of acetic acid through fermentation has been
known to mankind for many centuries (aerobic fermentation to vinegar, vin-aigre,
“acidic wine”), bulk acetic acid is produced by carbonylation of methanol, with both
methanol and carbon monoxide (from syngas) produced at low cost from natural gas.
37
Figure 11 is the BEOGP chart for acetic acid obtained from natural gas and from
fermentation. The dot-lined arrows and dash-lined arrows indicate, respectively, the
reduction of acetic acid profited-cost induced by scaling up the production plant to bulk
size and by cost reductions for fermentable sugar and fermentation microorganisms and
nutrients. It is evident that, despite the above cost reductions, no cost convergence is to
be expected for petrochemical and bio-based acetic acid. One reason is the initial offset:
at current parameters, bio-based bulk acetic acid is assessed as three times more
expensive than petrochemical acetic acid; another reason is that, contrarily to all cases
examined so far, the slope of the profited-cost vs. gas-price graph is steeper for biobased acetic acid than for petrochemical acetic acid. This is due to the large amount of
steam, assumed to be raised on fossil fuels, required for the production of bio-based
acetic acid. In principle, in an integrated bio-refinery43 steam could be raised by burning
biomass; however, the cost gap between petrochemical and bio-based acetic acid is at
this stage far too large to speculate on fine process improvements: a major breakthrough in the technology is called for before bulk acetic acid can be produced form
renewable resources at competitive costs.
200
Profited Cost (USc/Kg)
160
Acetic Acid
Bio-AA-2000
Bio-AA-Bulk-2000
Bio-AA-B-2020
120
80
40
4
6
8
10
Gas Price (US$/GJ)
Figure11: BEGSP chart for bio- to petrochemical acetic acid
43
National Renewable Energy Laboratory 2003 Research Review, NREL/BR-840-36178
38
5.3.2 Hydrogen
The economics of bulk hydrogen production from renewable resources are
compared to those of the traditional petrochemical production by steam reforming of
methane.
Figure 11 is the BEOGP chart for hydrogen obtained from natural gas and potato
peels (food industry waste). The dot-lined arrows indicate the reduction of hydrogen
profited-cost induced by scaling up the production plant to bulk size; the thick-lined
arrows indicate two BEGP’s for hydrogen produced from natural gas and form biofeedstock: B2 (12.3US$/GJ) is the BEGP for a bio-based plant capacity of 0.3 KT and
B1 (7.2US$/GJ) is the BEGP for a bulk-capacity bio-based plant.
Profited Cost (USc/Nm3)
24
20
Hydrogen
Bio-H2
Bio-H2-Bulk
16
12
B1
4
6
8
B2
10
12
14
Gas Price (US$/GJ)
Figure12: BEGSP chart for bio- to petrochemical hydrogen
The graph of Fig.12 indicates that the production of hydrogen based on bio-feedstock
might become competitive with steam reforming of methane under the condition of
high fossil fuel prices. In terms of scenario projections, such high break-even gas prices
could be reached between 2040 and 2050 in the high long-term gas price scenarios A1
and A2. Table 5.IV is a summary of the relevant parameters, relative to year 2000, used
to calculate the profited cost of the natural gas based materials presented in this section.
39
Hydrogen
A2
Profited Cost (USc/Nm3)
18
A1
16
Bio-Bulk
B2
14
B1
12
10
2000
2010
2020
2030
2040
2050
Year
Figure13: Profited-cost projection for hydrogen based on fossil and renewable resources in four scenarios
Bio-AA
31
915
23.6 3,45
3.6
77.6
18.7
Bio-AABulk
310
457
23.6
1.7
1.8
60.8
18.7
Pet-AA
540
340
3.3
1
1.1
17.3
Bio-H2
0.32 2560
10
57
45.4
85
3.1
Bio-H2Bulk
3.2 1280
10
28.5
22.7
85
3.1
Pet-H2
6 172.5 -2.91 7.056 6.608 68.32
Natural Gas (Us$/GJ of
feedstock)
Potato Steam Peels
(Usc/Kg of feedstock)
Dextrose (Usc/Kg of
feedstock)
Carbon Monoxide
(Usc/Kg of feedstock)
Total Material costs
(Usc/Kg)
Methanol (Usc/Kg of
feedstock)
Overhead and taxes
(Usc/Kg)
Labour and materials
(Usc/Kg)
15.2
Investment costs
(US$/ton capacity)
18.8
Plant capacity
(KT/year)
Utility costs (Usc/Kg)
Table 5.IV: Relevant parameters for calculating profited cost of acetic acid and hydrogen (year 2000) 44
3.63
44
Petrochemical data: SRI Yearbook 2000, Biotech data: confidential industry sources for the BREW
project (BREWtool)
40
6. DISCUSSION AND CONCLUSION
This report, without having the ambition to give a definitive answer to it, addressed
the research question of identifying a set of bulk chemicals based on renewable
resources having high potential to become economically competitive with their
petrochemical analogues in the short or mid term.
This paper contributes to the BREW project by proposing a standard methodology
to estimate the profitability of products and production routes to products, and to project
the cost of chemical products in relation to the price of feedstock and energy carriers;
this paper also describes the database of petrochemical process economics included in
the calculation tool BREWprofit, intended as an input instrument to the BREW project
to estimate the future cost of a set of relevant bulk petrochemical products. A
preliminary assessment of the mid- to long-term potential market competitiveness of
selected bulk chemicals based on renewable resources was also carried out, providing a
set of interim conclusions.
During the conduction of this study, a series of research questions were examined
and tested, for feasibility and relevance, against the problems encountered; it is
appropriate to discuss briefly a few research questions that were ultimately not pursued
and that therefore are not documented in the main body of this report.
The first instance in which a research idea did not yield positive results was the
attempted development of an empirical, simplified method to estimate profited cost
variations in relation to fossil fuel prices. The underlying idea was that it might be
possible to link profited cost dependences on oil price to simple parameters, such as list
price of the chemical, and in this way come up with future profited cost projections
without the need to go through the detailed analysis of the cost structure. This
possibility was tested by plotting the profited cost increase of all chemicals included in
BREWprofit and looking for relationships.
All values (for oil-derived bulk chemicals, excluding chemicals, such as acetic acid
and methanol, primarily based on natural gas) were between 8 and 18 US$/ton profited
cost increase per 1 US$/barrel oil price increase45, with most values in the range 11-15
US$/ton, with a dependence on market price much weaker than the data scatter. The
only conclusion one could give to the attempt of devising the simplified method
45
these figures refer to profited cost increases determined by oil price increase including gas, coal and
utilities prices increasing in accordance with model relationships between oil price and gas, coal and
utilities prices.
41
outlined above is that all oil-derived bulk chemicals should experience a profited cost
variation of 135 (2)46 US$/ton per 1 US$/barrel oil price increase, regardless of any
specificity of the bulk chemical.
In all cases profited costs (roughly equivalent to minimum selling prices) are
calculated with 20% capital charge. One could be tempted to try to tune the capital
charge parameter in order to calculate a profited cost that may be used as much as
possible as market price proxy. This attempt was soon abandoned, as it became evident
that it would not be successful, for several reasons: first of all, profit margins are not the
same for all products and they are not constant in time but they depend on economic
contingencies; secondly, market prices are determined not only by production costs but
also by market forces. Ethylene and propylene, for instance, can be considered as
products of the same process (naphtha steam-cracking); this implies that their
production costs have the same dependence on all such variables as feedstock and
labour cost or technology improvements. None the less, the price ratio between ethylene
and propylene has changed by approximately 40% over the last 4 years, due to strongly
increasing polypropylene demand. Since technical parameters alone cannot account for
these effects, they are not sufficient to calculate market prices.
It is nevertheless interesting to compare some of the profited costs shown in chapter
5 with the current market prices: the profited costs calculated for Acetone, MEK,
Polyethylene are ca. 20% lower than the respective bulk list prices, the profited cost
calculated for PET is very close to its list price, whereas the list price of acetic acid is
more than twice its calculated profited cost. Different market price/profited cost ratios
were observed for other chemicals, in certain cases the calculated profited cost resulted
even in significant excess of the market price; generalizations proved hard to make.
Ultimately, calculating profited costs with 20% capital charge was chosen as a
relevant indicator able to assist a hypothetical chemicals company to decide whether it
will be a better (more profitable) investment to build a new petrochemical plant to
produce a certain bulk chemical, or to build a new plant producing the same bulk
chemical, or a replacement chemical having the same function, based on renewable
resources.
A normal (Gaussian) probability distribution is assumed.  is a parameter related to the width of the
probability distribution function; ca. 95% of the data fit within 2 , while ca 66% of the data fit within 
(in this case 132.5 US$/ton).
46
42
This approach was proposed in the methodological section of this paper and adopted
for concrete case studies in chapter 5. Preliminary comparative cost analysis on a series
of bulk chemicals put in evidence the different prospects of a set of selected bulk
chemicals based on renewable resources. In some cases (polylactides as a replacement
of PET, ethyl lactate as a replacement of a number of solvents) there are concrete
opportunities to be exploited in the near future. In other cases (Hydrogen from food
industry waste, bio-derived PTT as a replacement of PET) the petrochemical equivalent
is expected to offer superior economic prospects for an extended amount of time, while
the bio-based replacement could only attain competitiveness in case of oil prices
stabilising at levels in large excess of the current ones. Such circumstances could well
take place during the course of the 21st century due to the gradual depletion of fossil
energy resources.
Explicit time-domain projections of the future profited cost of the examined
chemicals based on fossil and renewable resources were obtained, until 2050, by
combining fossil fuel price projections from the IPCC-SRES scenarios with the
BREWprofit model.47 A simple set of four scenarios was used (SRES scenario families
A1, A2, B1, B2), two of which (A1, B1)48 are associated with relatively low (<50
US$/barrel) long-term oil price, and the other two (A2, B2)49 with oil price stabilising
in the long-term on a high level (>70 US$/barrel). Under scenarios A1 and A2,50
petrochemical hydrogen is always (until 2050) expected to remain cheaper than biobased hydrogen, polyethylene cheaper than polylactides and PET cheaper than PTT.
Under scenarios A2 ad B2, conversely, concrete windows of opportunity for these
replacements could be opened starting from the year 2030.
Finally, the case-studies presented in this report also included products, such as
acetic acid, that, despite the well-known existence of a relatively efficient bio-synthetic
route, must compete with petrochemical competitors that are so cheap that windows of
opportunities are not foreseen at all for bio-synthetic production unless further major
break-through in the specific biotechnology occurs.
The acetic acid case also evidenced the aspect of energy use for the conversion of
renewable resources into bulk chemicals: although the progress of modern
47
These projections, especially since they span such a long time frame, should be taken as indications of
the outcome of a set of assumed circumstances; it would be a mistake to regard them as predictions.
48
These two scenarios are based on the concept of a global-oriented future world
49
These two scenarios are based on the concept of a regional-oriented future world
50
Petrochemical hydrogen is assumed to be produced from natural gas, and long-term gas prices are
highest in the “A” family scenarios.
43
biotechnology contributed to significantly reduce such energy inputs, the same question
marks that stand before the large-scale diffusion of bio-fuels are also largely valid for
bulk chemicals. For bio-fuels a minimum, although not only one, discriminator applies,
i.e. that no more fossil fuel input, from a life-cycle perspective, should go into the biofuel than the energy content of the same bio-fuel; in the case of bulk chemicals, it
should be made sure, in order for a biotechnology to be sound, that less fossil energy be
required for processing than for the production of the petrochemical equivalent. One
successful example in this sense, although not devoid of ancillary complications,51 is
the production of low-cost bio-ethanol in Brazil: the energy required for distillation is
entirely obtained from burning the sugar-crop waste (bagasse), with excess electricity
being sold out to the electric grid as by-product. These ideas lead to the concept of biorefinery, a largely self-sufficient complex that combines integrated energy generation
and production of whole chains of chemicals with efficient logistics for feedstock
handling, with the expected effect of cutting down, not differently from the way it
happens in petrochemical refineries, at the same time production costs and
environmental impacts.
51
Brazilian sugar-cane crop has not always been regarded as sustainable.
44
APPENDIX I: Approximating Naphtha price
Although substitution by other feedstocks (ethane, gas oil) is possible, in Europe
olefins are mainly produced by steamcracking of naphtha; for this reason it is important
that the relationship between oil price and naphtha price be modelled reliably, as errors
would propagate through most of the whole petrochemical product chain included in
this study.
Historically, the correlation between bulk chemicals price and oil price has been
rather erratic, as production costs are only one of the factors, together with market
forces, determining product prices. Calculating market prices simply on the basis of
techno-economic parameters is not feasible; hence the present model calculates profited
costs rather than attempting to reproduce market prices.
This is not true in the case of naphtha. Naphtha is one of the main refinery
commodities; it is produced in enormous amounts as a fraction of atmospheric
distillation, and profit margins are limited, the price per ton being only of the order of
25% higher than that of crude oil. Figure 14 shows 24 naphtha prices, taken at intervals
of six months from 1992 to 2001, plotted against the relative crude oil prices (Brent).
The correlation, over an oil price range spanning a factor four, was deemed sufficient to
fit naphtha price to oil price, with parameters reported in figure.
40
Naphtha Price (US$c/Kg)
Naphtha Price= 2.5+0.82*Oil Price
30
20
10
10
20
30
40
Oil Price (US$/Barrel)
Fig. 14: Correlation between Oil price and Naphtha price. Data source: Platt’s
45
APPENDIX II: Electricity price calculation and Utility prices
Electricity price was calculated as the electricity generation cost including fixed
capital depreciation, averaged across the energy mix (share of gas, coal, oil, nuclear,
biomass and hydropower) in use for power generation in the EU in the year 2000,
assumed as constant. Techno-economic data for power generation by each of the energy
carriers were used as published by ECN.52 These data include: Investment costs, energy
conversion efficiency, fixed costs, variable costs other than fuel, workload (hours of
operation per year).
Capitalisation was done by assuming a 10% discount rate (r) over a depreciation
period (DT) of 15 years,53 corresponding to 13.5% annuitised capital charge (a),
according to the standard formula:
a
8)
r
1  (1  r )  DT
Fossil fuel prices were input from SRES scenarios, biomass price was 1.5US$/GJ
(see section 4.3) and nuclear fuel price was set as constant 0.7 US$/GJ;53 although
predictions of future nuclear fuel prices are not easy to make, fuel only accounts for a
minor part of nuclear power generation costs. Assuming nuclear fuel price as constant,
therefore, only propagates a small error on electricity generation cost.
Table A2.I presents an overview of the adopted parameters with fuel costs referring
to year 2000.
Table A2.I: Summary of Electricity Pricing.
ENERGY CARRIER
Share in Energy Mix
Fuel Cost
Conversion Efficiency
Investment Costs
Fixed Costs
Variable Costs
Workload
Capital Charge
Total Fixed Costs
Total Variable Costs
Total Cost including
depreciation
Average
Electricity Price
%
US$/GJ
%
US$/kW
US$/kW
USc/kWh
(hours/Y)
%
USc/kWh
USc/kWh
Gas
6.21
3.63
55
510
10
0.2
7000
13.5
1.13
2.58
Coal
39.7
1.35
38
1125
35
0.2
7000
13.5
2.67
1.48
USc/kWh
3.70
4.15
USc/kWh
Oil
Nuclear Biomass
6.47
30.37
1.32
4.45
0.7
1.5
45
35
38
825
1710
1600
20
57.5
43
0.1
0.6
0.2
7000
7000
7000
13.5
13.5
13.5
1.88
4.12
3.70
3.66
1.33
1.62
5.54
5.45
5.32
Hydro
15.93
0
NA
1850
30
0
4500
13.5
6.22
0.00
6.22
4.95
Smekens K.E.L, Lako P, Seebregts A.J, “Technologies and technology learning, contributions to IEA's
Energy Technology Perspectives”, ECN-C--03-046, August 2003
53
Parameters reported by Framatome at the UNECE Roundtable Facilitating Investment in the
Electricity Sector in the Transition Economies, Geneva, 19. Nov. 2003
http://www.unece.org/ie/se/pp/elec/framatome.pdf
52
46
This method resulted in a baseline (year 2000) utility cost of 5.45 US cents per
kWh, matching with the reported EU mean electricity price to industry for the same
year.54 For consistency with BREW’s standard utility prices, since the datum on
variable costs for nuclear power generation was missing, this parameter was used as
dummy to make a minor (~0.1 US cents/kWh) adjustment of the final electricity price
to the expected value. This implies assuming a variable cost of 0.6 US cents/kWh, i.e.
three times the figure reported for coal-fired stations. Table A2.II is a summary of base
(year 2000) utility prices standardised for the BREW project, in US$.
Table A2.II: Standard Base Utility Prices for BREW.
UTILITY
Electricity
Steam
Cooling Water
Process Water
Natural Gas
Refrigeration
Compressed Air
Inert Gas
Oxygen
COST
54.5
10.9
0.045
0.23
3.64
22.7
5.45
27.3
45.5
UNIT
US$/MWh
US$/t
US$/t
US$/t
US$/GJ
US$/MWh
US$/1000 Nm3
US$/1000 Nm3
US$/t
SOURCE BREW
Eurostat
Industry sources
Industry sources
Industry sources
Industry sources
Industry sources
Industry sources
Industry sources
Industry sources
“European Union Energy & Transport in Figures 2003”, European Commission, Directorate-General
for Energy and Transport, in co-operation with Eurostat:
http://europa.eu.int/comm/dgs/energy_transport/figures/pocketbook/doc/en_prices_2003.pdf
54
47