Evaluation of economic feasibility under uncertainty of a thermochemical route for ethanol production in Brazil

Accepted Manuscript Evaluation of economic feasibility under uncertainty of a thermochemical route for ethanol production in Brazil

Reynaldo L.N. Taylor-de-Lima, Arthur José Gerbasi da Silva, Luiz F.L. Legey, Alexandre Szklo PII:

S0360-5442(18)30352-9

DOI:

10.1016/j.energy.2018.02.118

Reference:

EGY 12418

To appear in:

Energy

Received Date:

21 September 2016

Revised Date:

31 January 2018

Accepted Date:

21 February 2018

Please cite this article as: Reynaldo L.N. Taylor-de-Lima, Arthur José Gerbasi da Silva, Luiz F.L. Legey, Alexandre Szklo, Evaluation of economic feasibility under uncertainty of a thermochemical route for ethanol production in Brazil, Energy (2018), doi: 10.1016/j.energy.2018.02.118

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ACCEPTED MANUSCRIPT

Evaluation of economic feasibility under uncertainty of a thermochemical route for ethanol production in Brazil Reynaldo L. N. Taylor-de-Limaa,c, Arthur José Gerbasi da Silvab,c,1, Luiz F. L. Legeyc, Alexandre Szkloc a José

Bonifacio University Foundation Senior Engineer at: Department of Chemical Processes,

School of Chemistry, Centre of Technology, The Federal University of Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Bloco E, Sala I-222, 21941-909, Ilha do Fundão, Rio de Janeiro, RJ, Brazil. E-mail: [email protected] b Petrobras

Research Centre - Avenida Horácio Macedo, 950, Cidade Universitária, Ilha do Fundão,

Rio de Janeiro, RJ - 21941-915, Brazil. E-mail: [email protected] c Department

of Energy Planning, Graduate School of Engineering (COPPE), The Federal

University of Rio de Janeiro (UFRJ), Centro de Tecnologia, Bloco C, Sala 211, Cidade Universitária, Ilha do Fundão, 21941-972 Rio de Janeiro, RJ, Brazil. E-mails: [email protected] , [email protected] Abstract This paper assesses the economic feasibility of ethanol and higher alcohols production through a thermochemical route in Brazil. This route comprises mainly sugarcane bagasse gasification, syngas cleaning, and synthesis of alcohols. Ethanol production costs and associated risks are evaluated in different scenarios. A case study of a virtual plant to be located adjacent to a sugarcane mill, in the state of São Paulo is presented with the objective of finding the minimum ethanol selling price (MESP) that makes the plant’s project economically feasible. The analysis

1

Corresponding author at: Petrobras Research Centre - Avenida Horácio Macedo, 950, Cidade

Universitária, Ilha do Fundão, Rio de Janeiro, RJ - 21941-915, Brazil Fax: (+55-21) 2162-1007. Phone: (+55-21) 2162-7168. E-mail: [email protected] 1

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utilizes first the traditional discounted cash flow method and then proceeds to use a stochastic approach, which is more suitable to study economic feasibility under uncertainty conditions. In the latter approach, prices of raw materials and products are modelled through a mean reverting stochastic process, and economic feasibility is analysed with the help of Monte Carlo simulations. Results are summarized via a histogram of the MESPs obtained for different simulated price scenarios. Finally, the project’s risk is evaluated by computing the number of instances in which the MESP is greater than the ethanol market price.

Keywords: Biomass conversion; thermochemical ethanol; economic feasibility; uncertainty; stochastic processes; mean reverting process

1. Introduction There is a growing consensus that liquid biofuels production technologies need to be more efficient in terms of net greenhouse gases emissions and, in addition, to have a greater social and environmental sustainability. First generation biofuels – with the exception of sugarcane ethanol – will probably have a limited participation in the future world’s energy matrix for the transport sector, because they use agricultural products inputs that should be directed to human consumption (Eisentraut (2010) [1], Fatih Demirbas (2009) [2], Intenational Energy Agency (2011b) [3], Sims et al (2010) [4]). This criticism has highlighted the importance of the second generation biofuels, which can be produced from agricultural residues — such as sugarcane bagasse and straw – or from plantations cultivated on currently unproductive land (Eisentraut (2010) [1], Fatih Demirbas (2009) [2], Naik et al (2010) [5], Sims et al (2010) [4], Huber (2008) [6]). In the Brazilian case, the production of ethanol from agricultural residues through the thermochemical route is particularly interesting due to its big flex-fuelled (ethanol, gasoline or any mixture of them) light vehicle fleet and the abundance of agricultural residues, especially from 2

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sugarcane (De Freitas and Kaneko (2011) [7]; Lora and Andrade (2009) [8]; Bonassa et al (2017) [9]). Different thermochemical routes for the production of ethanol from syngas have been studied, but the one considered most promising is based on the direct conversion of syngas to ethanol and higher alcohols with alkali promoted molybdenum sulphide based catalysts (Subramani and Gangwal (2008) [10], Surisetty et al (2011) [11],Fang et al (2009) [12], Gerbasi da Silva et al (2014) [13], Andersson (2015) [14]). In this context, it is of paramount importance to analyse whether the production of ethanol from agricultural residues through the thermochemical route is economically feasible. Along those lines, the techno-economic feasibility of this route was studied by many authors (Koch (2008) [15], Walter and Ensinas (2010) [16], Villanueva Perales et al (2011) [17], Van der Heijden and Ptasinski (2012) [18], Reyes Valle et al (2013) [19], Seabra et al (2010) [20], He and Zhang (2011) [21], Patel et el. (2016) [22], Santos et al (2016) [23]), with special emphasis to researchers working for NREL (National Renewable Energy Laboratory – USA), as Phillips (2007) [24] and

Phillips et al (2007) [25], who evaluated the thermochemical conversion of biomass (wood) to ethanol, using indirect steam gasification, syngas cleaning and alcohols synthesis in a reactor pressurized to 6.9 MPa; these authors calculated the minimum ethanol selling price (MESP), using the methodology described by Aden et al (2002) [26]. The indirect gasification process was chosen

because it was believed that this process had techno-economic advantages when compared to the direct gasification process (later confirmed by Dutta and Phillips (2009) [27]) and to the entrained flow gasification process (later confirmed by Dutta et al (2010) [28]). Dutta et al (2011) [29] and Dutta et al (2012) [30] proposed several improvements to the thermochemical conversion of biomass to ethanol process suggested by Phillips (2007) [24] and Phillips et al (2007) [25], using the same methodology to evaluate it. Because it was “the most detailed description of a thermochemical process to convert biomass to ethanol” available, da Silva (2013) [31] adopted Dutta et al (2011) [29] as a reference to evaluate the thermochemical 3

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conversion of sugarcane bagasse to ethanol and higher alcohols, based on catalysts developed in Brazil (da Silva (2013) [31], da Silva et al. (2012) [32] and Gerbasi da Silva et al. (2014) [13]). da Silva found that another advantage of using the work of Dutta et al. (2011) [29] as a reference is that the biomass, synthesis catalyst performance and plant scale selected by da Silva [31] are similar to those selected by Dutta (Dutta et al. (2011) [29]), which makes it unnecessary to model and simulate the process to size the plant equipment. This choice makes it possible to use the equipment costs obtained by NREL, which are publicly available, as a reference for the Brazilian case. This advantage is of utmost importance, because the model used by NREL is not publicly available. In the context of stochastic modelling, the first issue to be addressed is the selection of the underlying stochastic process. To this end a popular process is the Geometric Brownian Motion process (GBM). However, when modelling price variations of raw commodities such as iron ore and crude oil, Dixit and Pindyck (1994) [33] argue that it would be more adequate to employ a Mean Reverting Process (MRP), since in the long run they tend to return to their marginal cost of production. Schwartz (1997) [34] studied three different mean reversion models, taking into account their ability to forecast prices of existing future contracts of commodities as compared to the pricing of other financial and real assets. Using estimated parameters for copper, crude oil and gold the study reveals a strong mean reversion property for these commodities prices. Baker-Mayfield-Parsons (1998) [35] explore the essential differences that arise from modelling commodity prices using random walk and mean reverting models. A simple one-factor mean reverting model is estimated for crude oil price series, as well as a two-factor model that combines features of the random walk and of the mean reverting models. Using Baker-Mayfield-Parsons (1998) [35] as a basis, Pindyck (1999) [36] examines the long-run behavior of crude oil, coal, and natural gas prices, using up to 127 years of data, in order to understand whether reversion models to stochastically fluctuating trend lines can be used to forecast prices over horizons of 20 years or more. 4

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Schwartz and Smith (2000) [37] proposed another two-factor model in which the logarithm of spot price is decomposed into two stochastic (state) variables: one representing short-term variations and another accounting for equilibrium prices. Parameters are estimated through a Kalman filter in which observed variables are future prices for different maturities. Aiube-Baidyac-Tito (2008) [38] argue that Schwartz and Smith (ibid.) [37] did not incorporate jumps in the short-term variation of prices, because then their variables would be non-Gaussian and so the Kalman filter should not be used. They demonstrate that the inclusion of jumps would explain better the behavior of oil prices, but would make parameter estimation more difficult. To overcome this difficulty, Aiube-Baidyac-Tito (2008) [38] propose an estimation method called the particle filter. Dias (2004 [39], 2005 [40]) illustrates the importance of considering the flexibility of investment by discussing the introduction of the real options approach in the petroleum industry. He studies different stochastic processes for the modelling of oil prices, which draws from the previous cited papers from Pindyck and Schwartz. Project investment flexibility under uncertainty conditions ― such as the possibility of switching outputs and inputs of productive processes ― becomes an important and valuable feature, whose value can be captured by the real options method. Thus, if demand or prices of products or raw materials fluctuate, there are alternatives such as switching to a more profitable product mix or to a lower cost input. Along those lines, Bastian-Pinto, Brandão, and Hahn (2009) [41], analyse the option to interchange the final product of a Brazilian sugar mill plant between sugar and ethanol, taking into account that their prices are correlated. Mean reverting stochastic processes are used for modelling prices of raw material and products, and the economic feasibility of a project is analysed with the help of Monte Carlo simulations. Ozorio, Bastian-Pinto, Baidya and Brandão (2013) [42] analysed investment decisions under uncertainty in a hypothetical integrated steel plant. They stress the importance of correctly choosing the stochastic process for modelling uncertainty and its effect on the value of the switch option. Results show that the availability of a switch option can generate a significant increase in the project’s NPV.

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Kulatilaka (1993) [43], Gonçalves et al (2006) [44], Bastian-Pinto et al (2010) [45], Brandão et al (2011) [46], Vianello-Costa-Teixeira (2014) [47], Costa-Samanez (2014) [48], Franco (2013) [49]

and Kulatilaka (1993) [43] analysed the value of flexibility for a double-fuel industrial steam boiler; Gonçalves et al (2006) [44] addressed the case of a switch option in an agribusiness investment project; Bastian-Pinto et al (2010) [45] valued the flexibility of a flex-fuel car, which can use either ethanol or compressed natural gas; Brandão et al (2011) [46] estimated the value of switching inputs in a biodiesel production plant; Vianello-Costa-Teixeira (2014) [47] and Costa-Samanez (2014) [48] studied dynamic models of uncertainty applied to investment in refining and petrochemical projects. In the present paper we analyse, with the help of Monte Carlo simulations, the economic feasibility of a second generation ethanol and higher alcohols thermochemical route production process. To model price uncertainty of both raw materials and products, a one-factor stochastic model of the Schwartz type (Schwartz, 1997) [34] is adopted. As in Dias (2005) [40], Costa-Samanez (2014) [48] and Vianello-Costa-Teixeira (2014) [47] it is assumed that the logarithm of the spot price of a commodity follows a mean reverting process. The maximum likelihood method is used for the estimation of parameters of the mean reverting process, along the lines proposed by Franco (2013) [49]. The paper is organized in four sections besides this Introduction. Section 2 presents the case of an ethanol and higher alcohols production plant, in which renewable syngas is obtained from the gasification of sugarcane bagasse. In section 3 a model for the evaluation of economic feasibility of the plant is described. Section 4 shows some of the results attained and discusses their implications. Finally, in section 5, some conclusions and considerations are made upon the issues brought up in the sections aforementioned.

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2. The Thermochemical Process of Ethanol Production in Brazil This section analyses the economic feasibility of the thermochemical route for outputting ethanol and higher alcohols in a virtual plant to be located adjacent to a sugarcane mill, in the State of São Paulo, Brazil, which employs renewable syngas obtained from the gasification of sugarcane bagasse. The analysis focus on a project that has an input of 2,000 dry tons of sugarcane bagasse per day, converted firstly into 1,560 tons of syngas per day and then, through catalytic synthesis, into 699,000 litres per day of ethanol and 84,200 litres per day of higher alcohols. The main data used in this paper is based on the work of da Silva (2013) [31], who adapted data from Dutta et al (2011) [29] to the Brazilian conditions (feedstock availability and cost, onstream factor, higher alcohols value, labor and capital costs and taxes) to assess the thermochemical process to convert sugarcane bagasse to ethanol and higher alcohols in a plant adjacent to a sugarcane mill in the State of São Paulo, Brazil. The analysis presented here follows the method of the nth industrial plant economic performance. This method ― developed in Swanson et al (2010) [50] and in Dutta et al (2011) [29], and used by da Silva (2013) [31] ― argues that the economic analyses of projects in pre-commercial stages should reflect the economic performance of the technology in its mature stages of production (Dutta et al, 2011 [29]). Swanson et al (2010) [50], applied this method to the techno-economic study of the conversion of corn stover into biofuels, through Fischer-Tropsch synthesis as did Taylor-deLima and Legey (2015) [51] for a sugarcane bagasse input. The thermochemical ethanol plant operation is divided into seven major process areas, namely: feed handling and drying; biomass gasification; synthesis gas cleanup; alcohol synthesis; alcohol separation; steam and energy generation ; and cooling water and other utilities. These process areas are described in detail in da Silva (2013) [31]. The corresponding capital expenditures are referred to each process area individually and are referred to 2007 US dollars. In the feed handling and drying area, capital expenditures are considered zero, because these costs are incorporated into the costs of raw materials (da Silva, 2013) [31].

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Table 1 shows the Fixed Capital Investment (FCI), as per the United States Gulf Coast (USGC), corresponding to US$ 491,340,560. This number, multiplied by an internalization factor of 1.7843, provides an estimation of the Fixed Capital Investment for Brazil (FCI-BR): US$ 876,698,961 (da Silva, 2013 [31]). In accordance to AACE International (2011) [52], in its Table 1, a typical Class 3 estimate for a process industry project may have an accuracy range as broad as -20% to +30%. In this sense, the estimation for the FCI, as per the USGC, can vary between a minimum of US$ 393,072,448 and a maximum of US$ 638,742,728. Accordingly, the estimation for the FCI-BR can vary between a minimum of US$ 701,359,169 and a maximum of US$ 1,139,708,650. Such values will be important in the analysis presented in section 4. Table 1 should be inserted. The FCI-BR is expended over three years, along the pre-operational period, in instalments corresponding to 8% (first year), 60% (second year), and 32% (third year) and its sources are: 40% from shareholders' equity and 60% from a 10 year loan with a long-term interest rate of 8% per year da Silva (2013) [31]). Revenues and operating expenditures calculations assume that for a processing capacity of 2,000 dry tons of sugarcane bagasse per day – equivalent to 175,208 dry tons per quarter, considering 8,410 plant operating hours per year or 350 operating days per year – the ethanol plant will produce per quarter 61,644,583 litres of anhydrous ethanol and 7,793,809 litres of higher alcohols2. In a first deterministic approach, the price of anhydrous ethanol is kept constant over time, equal to US$ 0.65 per litre. This value represents a good estimate for a long-term price for anhydrous ethanol in the domestic market (producer side), as shown in Figure 1 (data provided by CEPEA/ESALQ [53]). Figure 1 should be inserted.

2

It is possible to operate the plant 8,410 hours per year if a part of the sugarcane bagasse

produced during the sugarcane milling season – which lasts around 4,000 hours/year – is stored (see [20], [59] and [16]). 8

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For higher alcohols, the estimate is based on da Silva (2013) [31], who sets the price for higher alcohols at US$ 0.711 per litre, based on the heating value of higher alcohols relative to gasoline and the forecasted gasoline price (assuming that higher alcohols could be sold as a fuel, blended with gasoline). Table 2 summarizes the calculation of the annual gross and net revenues for the ethanol plant studied. Table 2 should be inserted. The price of the sugarcane bagasse is estimated as the opportunity cost for power generation assuming an electricity price of US$ 84.07 per MWh, which gives a price of US$ 35.0 per dry ton (da Silva, 2013 [31], and Sennejunker, 2012 [54]). The price of other inputs and utilities can be found in Dutta et al (2011) [29]. Table 3 lists the prices of products, inputs and utilities and Table 4 details the figures used in the computation of the annual variable operating cost of the ethanol plant. The labor cost, shown in Table 5, was calculated on the basis of the effective personnel recommended by Dutta et al (2011) [29] , with wages adapted to the Brazilian context (da Silva, 2013 [31]). Table 6 presents the annual fixed operating cost decomposed in labor cost and other fixed costs. Table 3 should be inserted. Table 4 should be inserted. Table 5 should be inserted. Table 6 should be inserted. With the data shown in Tables 1 to 6, it is possible to build a discounted cash flow for the project, for a 25 year horizon, which is adequate for the amortization of the investment in an industrial plant such as the one studied here. Table 7 shows a summary of the results obtained considering two different ethanol selling prices and a discount rate of 9% per annum (p.a.), which is usually adopted in Brazil for projects with a sustainability bias.

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Table 7 should be inserted. Considering an anhydrous ethanol price of US$ 0.65 per litre (anhydrous ethanol market price), the project’s NPV is negative. On the other hand, if the price of ethanol is assumed to float, it is possible to find the MESP which would lead to a null NPV. In the case studied, this MESP is equal to US$ 0.704 per litre, or 8.3% higher than the adopted market price.

3. A model for assessment of economic feasibility under uncertainty The methodology adopted in this study is based on the Ornstein-Uhlenbeck stochastic process, which is a kind of mean reverting process (MRP). A stochastic process is characterized by a random variable whose value changes over time in an uncertain way (Hull, 2012, p. 280) [55]. At time 𝑡, the random variable is represented by 𝑋(𝑡), which is also called the state of the process at time 𝑡. Prices of commodities – such as crude oil and its derivatives – tend to be associated with a longterm equilibrium price. The reason for the existence of such equilibrium price is the following: if prices become too high, companies will become more capitalized, thus increasing investments in future supply, which in turn lead to lower prices. On the other hand, when prices are low, investments are discouraged, thus causing a reduction in future supply, which leads to higher prices. In summary, in the short run, prices of a commodity behave randomly, whereas in the long run, tend to reverse to an equilibrium value, usually associated to the commodity’s (long run) marginal cost of production. This type of behaviour can be adequately modelled by the OrnsteinUhlenbeck mean reverting process (MRP). The MRP follows the Markov chain property, formally written as 𝑃(𝑋(𝑡 + 1) 𝑋(1), 𝑋(2),…𝑋(𝑡)) = 𝑃(𝑋(𝑡 + 1) 𝑋(𝑡))

(1)

Stochastic processes that follow a reverting movement to an arithmetic average, can be represented generally by the Schwartz model (1997) [34]: 𝑑𝑋(𝑡) = 𝜂(𝑋 ‒ 𝑋(𝑡))𝑑𝑡 + 𝜎 𝑑𝑧(𝑡) 10

(2)

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where 𝜂 is the speed of reversion to the mean, 𝑋 is the balance level or long-term mean of 𝑋(𝑡), 𝜎 is the volatility and 𝑑𝑧(𝑡) represents the increment of a Wiener process, given by the expression 𝑑𝑧(𝑡) = 𝜀𝑡

𝑑𝑡

(3)

in which the 𝜀𝑡 (∀𝑡) are independent and identically normally distributed random variables (i.i.d.), i.e., 𝜀𝑡~𝑁(0, 1). If 𝑋0 = 𝑋(0), the statistic properties of the MRP can be characterized by the expected value 𝐸[𝑋𝑡] of the variable 𝑋(𝑡) at time 𝑡 and by its variance 𝑉𝑎𝑟[𝑋𝑡] , given respectively by

𝐸[𝑋𝑡|𝑋0] = 𝑋 + (𝑋0 ‒ 𝑋) 𝑒

‒ 𝜂𝑡

(4.𝑎)

2

𝑉𝑎𝑟[𝑋𝑡|𝑋0]

𝜎 (1 ‒ 𝑒 ‒ 2𝜂𝑡) = 2𝜂

(4.𝑏)

As a consequence, the (random) value of the variable 𝑋(𝑡) at time 𝑡 is determined by the expression 2

𝑋(𝑡) = 𝑋0 𝑒

‒ 𝜂𝑡

+ 𝑋 (1 ‒ 𝑒

‒ 𝜂𝑡

)+

𝜀𝑡

𝜎 (1 ‒ 𝑒

‒ 2𝜂𝑡

)

(5)

2𝜂

From (4.b), one can observe that, for high values of the reversion speed ( 𝜂 → ∞), the process variance tends to zero, indicating that 𝑋 never deviates from 𝑋. Conversely, if the speed of 2

reversion tends to zero ( 𝜂 → 0), the expression for the variance becomes simply 𝜎 𝑡, which is the variance of the (non-mean reverting) geometric Brownian motion process. The parameters 𝑋, 𝜂 and 𝜎 of the mean reverting process are determined through maximum likelihood estimation (Franco,2013) [49]. Taking into account that the Ornstein-Uhlenbeck process defined in (2) is Gaussian, in the sense that for each 𝑋0 and 𝑡 the variable 𝑋(𝑡) follows a normal distribution 11

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with mean and variance given by (4.a) and (4.b), the conditional density 𝑓𝑖 of 𝑋𝑖 = 𝑋𝑡 , relative to 𝑋𝑖 ‒ 1 𝑖

= 𝑋𝑡

, can be expressed, for 𝑡𝑖 ‒ 1 < 𝑡𝑖 , by

𝑖‒1

𝑓𝑖(𝑋𝑖;𝑋;𝜂;𝜎) =

= (2𝜋)

[

‒1 2

2

𝜎

2𝜂

(

1‒𝑒

‒ 2𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1)

)

]

[

‒1 2

𝑋 ‒ 𝑋 ‒ (𝑋 𝜂( 𝑖

. 𝑒𝑥𝑝 ‒

2

𝜎

𝑖 ‒ 1 ‒ 𝑋) 𝑒

1‒𝑒

‒ 𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1) 2

)

‒ 2𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1)

]

(6)

Considering 𝑛 + 1 independent observations of (𝑡): 𝑋0, 𝑋1,…, 𝑋𝑛, for 𝑡0, 𝑡1,…, 𝑡𝑛, and their conditional densities 𝑓𝑖(𝑋𝑖|𝑋𝑖 ‒ 1; 𝑋, 𝜂,𝜎), for 𝑡1,…, 𝑡𝑛, the likelihood function is given by

𝐿(𝑋1,…,𝑋𝑛;𝑋;𝜂;𝜎) =

𝑛

=

∏ 𝑖=1

{

(2𝜋)

[

‒1 2

2

𝜎 ‒ 2𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1) 1‒𝑒 2𝜂

(

)

]

[

‒1 2

𝑋 ‒ 𝑋 ‒ (𝑋 𝜂( 𝑖

. 𝑒𝑥𝑝 ‒

2

𝜎

𝑖‒1 ‒ 𝑋

1‒𝑒

)𝑒

]}

‒ 𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1) 2

)

‒ 2𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1)

(7)

Taking the natural logarithm of 𝐿(𝑋1,…,𝑋𝑛;𝑋;𝜂;𝜎) and after some algebraic manipulations (see Taylor-de-Lima and Legey, 2015 [51]), the logarithmic form of likelihood function can be written as

𝑛

2

[ ] 𝜎

1

[

𝑛 Θ(𝑋1,…,𝑋𝑛;𝑋;𝜂;𝜎) = ‒ 2ln 𝜋 𝜂 ‒ 2∑𝑖 = 1ln 1 ‒ 𝑒

‒ 2𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1)

‒ 12

𝜂

𝑛

]‒

∑ 𝜎

(𝑋 ‒ 𝑋 ‒ (𝑋

2 𝑖=1

𝑖

𝑖‒1 ‒ 𝑋

1‒𝑒

)𝑒

‒ 𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1) 2

‒ 2𝜂(𝑡𝑖 ‒ 𝑡𝑖 ‒ 1)

)

(8)

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To estimate the parameters 𝑋;𝜂;𝜎 it is then necessary to solve the following maximization problem:

Max Θ(𝑋1,…,𝑋𝑛;𝑋;𝜂;𝜎)

(9)

𝑋,𝜂, 𝜎 ≥ 0

This maximization problem can be solved numerically by the generalized reduced gradients (GRG) algorithm. However, to proceed with this it is necessary to find the values 𝑋𝑖 = 𝑋𝑡 for 𝑖 = 1, …,𝑛 𝑖

using the values 𝑃𝑖 = 𝑃𝑡 of the observed price time series. 𝑖

Dias (2005) [40] suggests two different methods for doing this. The first one simply takes 𝑋(𝑡) = ln 𝑃(𝑡), but in this case, 𝑃 ≠ exp (𝑋) and 𝐸[𝑃(𝑡)] ≠ 𝑒𝑥𝑝(𝐸[𝑋(𝑡)]). The second method is preferred because it assumes that 𝑃 = exp (𝑋) and 𝐸[𝑃(𝑡)] = 𝑒𝑥𝑝(𝐸[𝑋(𝑡)]), which leads to the following relation between 𝑋 and 𝑃:

[

𝑃(𝑡) = 𝑒𝑥𝑝{𝑋(𝑡) ‒ (0,5)𝑉𝑎𝑟[𝑋(𝑡)]} = 𝑒𝑥𝑝 𝑋(𝑡) ‒

2

𝜎 (1 ‒ 𝑒 ‒ 2𝜂𝑡) 4𝜂

]

(10)

Once the parameters of the stochastic process are estimated, different synthetic future prices series can be generated and thus a different NPV can be found for each prices series. This technique is a Monte Carlo simulation, which in this case is characterized by the following sequence of operations: (i) construction of a basic future cash flow model as a function of programmed investments and expected revenues, as described in section 2; (ii) sampling of values of the normally distributed random variables, which are then used in the computation of a particular simulation scenario; (iii) computation of the project’s NPV for the particular simulation scenario defined in (ii); (iv) repetition of this sequence of operations for a large number of different scenarios, in order to obtain an estimated probability distribution (histogram) for the project’s NPV.

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4. Results and discussion Monte Carlo simulations results were expressed either in terms of the project’s NPV or of the MESP. The initial prices 𝑃0 were taken from market prices expressed in 2007 US$, and are the same as those in Table 3, i.e.: US$ 35.0 per dry ton for sugarcane bagasse; US$ 0.650 per litre, for anhydrous ethanol; and US$ 0.711 per litre, for higher alcohols. The parameters 𝑃 = exp (𝑋), 𝜂 , and 𝜎 were estimated according to the methodology described in section 3, using data from the price time series shown in Figures 2, 3 and 4. Table 8 summarizes the values of the parameters used in the simulations, expressed in 2007 US$ by means of the US GDP implicit price deflator [56]. Figure 2 should be inserted. Figure 3 should be inserted. Figure 4 should be inserted. Table 8 should be inserted. The Monte Carlo simulations generated 5,000 different scenarios for the price trajectories of biomass (sugarcane bagasse), anhydrous ethanol and higher alcohols (Figure 5). As an example of a particular scenario (number 704), Figure 6 shows the three price trajectories for these variables, which in this case corresponded to a positive NPV. Figure 5 should be inserted. Figure 6 should be inserted. Table 9 presents a summary of the results of the first set of Monte Carlo simulations, obtained with a mean Fixed Capital Investment for Brazil (FCI-BR) of US$ 876,698,961, which corresponds to a discounted value of US$ 724 million. These results indicate a positive mean NPV of US$ 227 million, even without considering any premium over anhydrous ethanol market price. Table 9 shows that among the 5,000 scenarios analysed, 4,245 presented a positive NPV, which translates into a

14

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percentage of scenarios of just 15.1% to find a negative NPV. Figure 7 presents the NPV histogram for the first simulation. Table 9 should be inserted. Figure 7 should be inserted. Varying FCI-BR, in accordance to AACE International (2011) [52], between a minimum value of US$ 701,359,169 (-20%) and a maximum of US$ 1,139,708,650 (+30%), as discussed in section 2, Monte Carlo simulations led to the results summarized in Tables 10 and 11. Results in Table 10, corresponding to FCI-BR min, show a positive mean NPV of US$ 304 million, with just 6.3% of scenarios presenting a negative NPV. In Table 11, which is based on FCI-BR max, results indicate a still positive mean NPV of US$ 55 million, but now with 43.6% of scenarios showing negative NPVs. Table 10 should be inserted. Table 11 should be inserted. In the second set of simulations, an 8.3% premium over anhydrous ethanol prices was imposed3, yielding a positive mean NPV of US$ 231 million. In this case, the estimated risk was about 14.9%, i.e., of the 5,000 scenarios simulated, only 745 had a negative NPV. The third set of simulations assumed that ethanol price could fluctuate, so that it was possible to find which would be the MESP for each simulation scenario. In other words, which would be the ethanol selling price that would make the project’s NPV equal to zero (or approximately equal to zero, since there was a tolerance of plus or minus US$ 1.0 million). A summary of the results obtained in this simulation is shown in Table 12. Table 12 should be inserted.

3

In the deterministic case of section 2, this 8.3% premium was the one that led to a zero NPV.

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It is interesting to note that a mean value of US$ 0.639 per litre was reached for the MESP, which is slightly smaller than 𝑃0, or US$ 0.650 per litre. From Table 10 it is also possible to see that among the 5,000 scenarios simulated, a total of 2,163 present a MESP greater than US$ 0.650 per litre, which means there is a 43.3% chance of having a MESP higher than the ethanol market price (𝑃0). On the other hand, this simulation results have shown that if a 8.3% premium over the ethanol market price is considered, thus increasing the market price to US$ 0.704 per litre, the chance of a MESP higher than this new 𝑃0 reduces to 30.4%. Figure 8 presents the MESP histogram for the third simulation. Figure 8 should be inserted. 5. FINAL REMARKS This paper analysed the economic feasibility under uncertainty of a thermochemical process for ethanol production in Brazil. The analyses encompassed both a traditional (deterministic) discounted cash flow approach and a stochastic modelling together with Monte Carlo simulations. The case study considered was a virtual ethanol plant adjacent to a sugarcane mill, in the State of São Paulo, that employs renewable syngas obtained from the gasification of sugarcane bagasse. In the first part of the analysis, the project’s discounted cash flow was built, assuming a 25 year lifetime and a discount rate of 9% per annum (p.a.). When an ethanol market price of US$ 0.65 per litre was adopted, a negative NPV was found. The project’s IRR was 7.6% p.a., thus lower – as one would expect – than the 9% p.a. usually accepted in Brazil for projects with a sustainability bias. By allowing the price of ethanol to float, so as to get a zero NPV, a minimum ethanol selling price (MESP) of US$ 0.704 per litre was found, which means a premium of 8.3% over the assumed ethanol market price. Results obtained from the stochastic modelling were expressed in terms of the project’s NPV. The first set of Monte Carlo simulations indicates a positive mean NPV of US$ 227 million, even without considering any premium over the market price for anhydrous ethanol. Moreover, among the 5,000 16

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Monte Carlo scenarios analysed, 4,245 had a positive NPV, which means a probability of just 15.1% of finding a negative NPV. A sensitivity analysis of the Fixed Capital Investment for Brazil (FCI-BR), assuming a range of -20% to +30% in accordance to AACE International (2011) [52], had shown a significant impact on NPV scenarios. Monte Carlo simulations indicated that a 20% lower FCI-BR led to a positive mean NPV of US$ 304 million, with just 6.3% negative NPV scenarios, whereas a 30% higher FCI-BR resulted in a still positive mean NPV of US$ 55 million, but now with 43.6% of the scenarios showing a negative NPV. The second Monte Carlo simulation assumed a premium of 8.3% over the anhydrous ethanol prices, which is precisely the premium which led to a zero NPV in the deterministic case. In this situation, the mean NPV increases to US$ 231 million and the probability of finding a negative NPV decreases to 14.9% (or 745 over 5000). In the third set of simulations, the ethanol price was allowed to float so that the MESP for each simulation scenario could be found. Results indicate that the mean MESP is US$ 0.639 per litre, which is slightly smaller than the ethanol market price of US$ 0.650 per litre. Moreover, it was also possible to see that, among the 5,000 simulated scenarios, a total of 2,163 presented a MESP greater than US$ 0.650 per litre, which means there is a 43.3% chance of having a MESP higher than the ethanol market price. When considering an ethanol market price of US$ 0.704 per litre (8.3% premium over the market price), the chance of a MESP higher than this new ethanol market price was reduced to 30.4%. In other words, under the assumptions made, the project is feasible in almost 70% of the possible price scenarios. It is important to stress that this study suggests that by considering uncertainty and stochastic modelling ― thus providing the decision maker with a broader basis to assess risk and returns of an investment project ― a more educated decision making can be achieved. Actually, it may happen that even in situations for which a deterministic approach would not recommend the investment, the consideration of risks and returns ― as well as the risk propensity of the decision maker ― can result in the project’s acceptance.

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The feasibility of the thermochemical process for ethanol production in Brazil should be periodically reassessed to take into account technological evolution and changes in price tendencies.

ACKNOWLEDGEMENTS The authors would like to thank Petrobras, the Chemistry School of the Federal University of Rio de Janeiro and CNPq for the support provided to this research.

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Tables and Figures

Table 1 – Thermochemical ethanol production plant – fixed capital investment - USGC basis THERMOCHEMICAL ETHANOL - CAPITAL INVESTMENT ( CAPEX ) Total Investment

Installed Costs USA ( US$ )

Process Areas Feed handling and dryinga Gasification Gas cleanup Alcohol synthesis Alcohol separation Steam plant and power Cooling water and other utilities Inside Battery Limits (ISBL)b Outside Battery Limits (OSBL) Total installed cost ( TIC ) Other direct costs Land ( not depreciated ) Site development ( % of ISBL ) Total direct cost ( TDC ) Indirect costs ( % of TDC ex Land ) Prorated expenses Home office and construction fees Field expenses Project contingency Other costs (start-up and permits) Fixed capital investment ( FCI ) a b

43.250.000 55.430.000 122.050.000 20.310.000 45.840.000 9.560.000 241.040.000 55.400.000 296.440.000

4,0%

10,0% 20,0% 10,0% 10,0% 10,0%

Capital costs included in feedstock price. Battery limits exclude "Feed handling and drying" and "Gasification" Areas.

24

1.610.000 9.641.600 307.691.600 183.648.960 30.608.160 61.216.320 30.608.160 30.608.160 30.608.160 491.340.560

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Anhydrous Ethanol Price (US$/L)

2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 nov-15

nov-14

nov-13

nov-12

nov-11

nov-10

nov-09

nov-08

nov-07

nov-06

nov-05

nov-04

nov-03

nov-02

0.0

Figure 1 – Anhydrous ethanol domestic market selling price Source: CEPEA/ESALQ [53]

Table 2 – Thermochemical ethanol production plant – annual gross and net revenues THERMOCHEMICAL ETHANOL - ANNUAL GROSS AND NET REVENUES

Scenario Items Annual Gross Revenue Anhydrous Ethanol Quantity Price Higher Alcohols Quantity Price ( - ) Taxes Taxes ( = ) Annual Net Revenue

OPERATIONAL PHASE

Unique Unity

1rst Quarter

Year 2 2nd Quarter 3rd Quarter

Liters US$ / Liter

61,644,583 0.6500

61,644,583 0.6500

61,644,583 0.6500

Liters US$ / Liter

7,793,809 0.7110

7,793,809 0.7110

7,793,809 0.7110

4th Quarter 182,441,509 160,275,915 61,644,583 0.6500 22,165,593 7,793,809 0.7110 182,441,509

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Table 3 – Thermochemical Ethanol – Prices of products, inputs and utilities

Product Ethanol Higher alcohols

PRICES OF PRODUCTS Unity ( US$ / Liter ) ( US$ / Liter )

Price 0,650 0,711

PRICES OF INPUTS Input Biomass ( sugarcane bagasse ) Olivine Magnesium oxide (MgO) Tar reformer catalyst Alcohol synthesis catalyst Solids disposal Diesel fuel Boyler feed water chemicals Cooling tower chemicals Caustic soda LO-CAT chemicals DEPG makeup Amine makeup

Utility Water makeup Water treatment

Unity 2007 US$ / dry ton 2007 US$ / ton 2007 US$ / ton 2007 US$ / Kg 2007 US$ / Kg 2007 US$ / ton 2007 US$ / Liter 2007 US$ / Kg 2007 US$ / Kg 2007 US$ / dry ton US$ / Liter of ethanol produced US$ / Liter of ethanol produced US$ / Liter of ethanol produced PRICES OF UTILITIES Unity 2007 US$ / ton 2007 US$ / ton

26

Price 35,00 224,23 473,36 39,11 65,48 49,22 0,76 5,00 2,99 143,72 0,000247 0,000138 0,000028

Price 0,28 0,68

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Table 4 – Thermochemical ethanol – Production plant annual variable operating costs THERMOCHEMICAL ETHANOL - ANNUAL VARIABLE OPERATING COSTS

Scenario

Unique

Items

Unity

Annual Variable Operating Costs Raw Materials and Inputs Biomass ( sugarcane bagasse ) Quantity Price Olivine Quantity Price Magnesium oxide (MgO) Quantity Price Tar reformer catalyst Quantity Price Alcohol synthesis catalyst Quantity Price Solids disposal Quantity Price Diesel fuel Quantity Price Boyler feed water chemicals Quantity Price Cooling tower chemicals Quantity Price Caustic soda Quantity Price LO-CAT chemicals Quantity Price DEPG makeup Quantity Price Amine makeup Quantity Price Utilities Water makeup Quantity Price Water treatment Quantity Price

OPERATIONAL PHASE Year 2 1rst Quarter

2nd Quarter

3rd Quarter

dry ton US$ / dry ton

175,208 35.00

175,208 35.00

175,208 35.00

ton US$ / ton

513 224.23

513 224.23

513 224.23

ton US$ / ton

7 473.36

7 473.36

7 473.36

Kg US$ / Kg

11,444 39.11

11,444 39.11

11,444 39.11

Kg US$ / Kg

18,501 65.48

18,501 65.48

18,501 65.48

ton US$ / ton

2,327 49.22

2,327 49.22

2,327 49.22

Liter US$ / Liter

79,548 0.76

79,548 0.76

79,548 0.76

Kg US$ / Kg

3,147 5.00

3,147 5.00

3,147 5.00

Kg US$ / Kg

1,144 2.99

1,144 2.99

1,144 2.99

dry ton US$ / dry ton

38 143.72

38 143.72

38 143.72

ton US$ / ton

61,644,583 0.00025

61,644,583 0.00025

61,644,583 0.00025

ton US$ / ton

61,644,583 0.00014

61,644,583 0.00014

61,644,583 0.00014

ton US$ / ton

61,644,583 0.00003

61,644,583 0.00003

61,644,583 0.00003

ton US$ / ton

159,669 0.28

159,669 0.28

159,669 0.28

ton US$ / ton

27,604 0.68

27,604 0.68

27,604 0.68

27

4th Quarter

32,793,033 32,536,949 24,529,167 175,208 35.00 459,848 513 224.23 12,640 7 473.36 1,790,321 11,444 39.11 4,845,874 18,501 65.48 458,135 2,327 49.22 240,404 79,548 0.76 62,943 3,147 5.00 13,687 1,144 2.99 21,930 38 143.72 61,000 61,644,583 0.00025 34,000 61,644,583 0.00014 7,000 61,644,583 0.00003 256,084 180,975 159,669 0.28 75,109 27,604 0.68

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Table 5 – Thermochemical ethanol – Production plant annual labor costs THERMOCHEMICAL ETHANOL - ANNUAL LABOR COSTS Total Labor Costs ( US$ / year ) 1.014.128 Position Title Salary ( 2007 US$ ) Positions Total Cost ( 2007 US$ ) Administration 169.583 Plant manager 147.000 1 147.000 Clerks and secretaries 7.528 3 22.583 Production 774.288 Plant engineer 70.000 1 70.000 Maintenance supervisor 57.000 1 57.000 Laboratory manager 56.000 1 56.000 Shift supervisor 48.000 5 240.000 Lab technician 8.364 2 16.728 Maintenance technician 8.364 16 133.824 Shift operators 10.037 20 200.736 Other services 70.258 Yard employees 5.855 12 70.258

Table 6 – Thermochemical ethanol – Production plant annual fixed operating costs THERMOCHEMICAL ETHANOL - ANNUAL FIXED OPERATING COSTS

Scenario Items Annual Fixed Operating Costs Labor Costs Labor costs - Operation ( 4 shifts ) Labor costs - Maintenance ( 1,6% ISBL p.y. ) Labor costs - Laboratory ( 20% Labor Operation.) Other fixed costs Benefits and general (90% Labor Cost ) Maintenance (3,0% FCI-BR p.y.) Insurance and taxes ( 0,7% FCI-BR p.y.)

OPERATIONAL PHASE Year 2

Unique Unity

1rst Quarter

2nd Quarter

3rd Quarter

4th Quarter 34,258,415 1,014,128

US$ US$ US$

US$ US$ US$

28

33,244,286 912,716 26,214,787 6,116,784

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Table 7 – Thermochemical ethanol – Discounted cash flow – Summary THERMOCHEMICAL ETHANOL - DISCOUNTED CASH FLOW - SUMMARY Items Present Value Present Value Case 1: Market Price

Case 2: Floating Price

1,367,632,734 1,367,632,734

1,467,159,432 1,467,159,432

1,367,632,734

1,467,159,432

Gross Revenue Domestic Sales Foreign Sales ( - ) Taxes ( = ) Net Revenue Total Cost ( - ) Fixed Cost ( - ) Variable Cost

403,184,364 155,906,627 247,277,737

( = ) EBITDA ( - ) Depreciation

964,448,370 1,063,975,068 308,988,699

( = ) Operating Profits

655,459,671

Total Expenditures ( - ) Comercial Expenses ( - ) Administrative Expenses ( - ) Financial Expenses ( Interests )

332,569,701 334,560,234 27,352,655 29,343,189 103,937,752 201,279,294

( = ) EBT ( - ) Taxes

322,889,971 130,069,808

( = ) Net Income ( + ) Depreciation ( - ) Fixed Capital Investment ( - ) Capacity Maintenance ( + ) Loan ( - ) Loan Amortization (+/-) Working Capital ( + ) Residual Value

192,820,163 257,194,031 308,988,699 723,716,049

( = ) Net Present Value (NPV)

-64,373,868

754,986,369

420,426,135 163,232,103

526,019,377 246,871,090 -29,825,220

Discount Rate

0 9.0%

Internal Rate of Return (IRR)

7.6%

9.0%

Ethanol Selling Price (ESP)

0.650

0.704

29

Anhydrous Ethanol Ex-works Price (US$/L)

ACCEPTED MANUSCRIPT

1.2 1.0 0.8 0.6 0.4 0.2 0.0 2003

2004

2005

2006

2007

2008

2009

2010

Quarter Mean (US$/L)

2011

2012

2013

2014

Quarter Mean (2007 US$/L)

Figure 2 – Anhydrous ethanol ex-works selling price (2003 – 2014)

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Quarter Mean (US$/L)

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0.0

1990

Regular Gasoline Price (US$/L)

Source: CEPEA/ESALQ [53]

Quarter Mean (2007 US$/L)

Figure 3 – Conventional Gasoline Regular – US Gulf Coast Spot Price FOB (1990 – 2014) Source: US Energy Information Administration ( EIA ) [57]

30

Electricity NEPOOL Price (US$/MWh)

ACCEPTED MANUSCRIPT

120 100 80 60 40 20 0

Quarter Mean (US$/MWh)

Quarter Mean (2007 US$/MWh)

Figure 4 – Electricity New England Power Pool ( NEPOOL ) Prices ( Quarter Mean ) Source: US Energy Information Administration ( EIA ) [58]

Table 8 – Stochastic Modelling Parameters MRP Parameters

P0 ( 2007 USD )

P ( 2007 USD )



σ

Sugarcane Bagasse

35.00

36.0754

1.21737

49.247%

Anhydrous Ethanol

0.650

1.0101

0.31491

33.507%

Higher Alcohols

0.497

1.5906

0.05645

32.106%

31

32

2049

2047

2045

2043

2041

2039

2037

2035

2033

2031

2029

2027

2025

2023

2021

2019

2017

Biomass Price (US$/dry ton)

ACCEPTED MANUSCRIPT

140

120

100

80

60

40

20

0

33

2047 2049

2047 2049

2049

2035

2033

2031

2029

2027

2025

2023

2021

2019

2017

2047

0.00 2045

0.50

2045

1.00

2045

1.50 2043

2.00

2043

2.50

2043

3.00 2041

Year

2041

0.00

2041

0.50 2039

1.00

2039

1.50

2039

2.00 2037

2.50

2037

2035

2033

2031

2029

2027

2025

2023

2021

2019

2017

Anhydrous Ethanol Price (US$/Liter)

Year

2037

2035

2033

2031

2029

2027

2025

2023

2021

2019

2017

Alcohols Price (US$/Liter)

Biomass Price (US$/dry ton)

ACCEPTED MANUSCRIPT

70.00

60.00

50.00

40.00

30.00

20.00

10.00

0.00

Year

Figure 6 – Price Trajectories – 704th Scenario – Biomass, Ethanol and Higher Alcohols

ACCEPTED MANUSCRIPT

Table 9 – First Monte Carlo Simulation – Summary – 5,000 Scenarios Scenario Items Minimum Maximum Mean 1 2 3 4 5

Risk NPV < 0

DISCOUNTED CASH FLOW FOR EACH SIMULATION ( MM US$ ) Gross Revenue 397 3,813 1,566 397 568 1,260 1,371 1,967

Fixed Cost 156 156 156 156 156 156 156 156

Variable Cost 160 311 239 165 243 247 236 216

Other Expenses 371 439 394 371 374 388 390 402

Taxes 1 932 193 1 3 90 119 321

Net Fixed Capital Income Investment (605) 724 1,750 724 274 724 (605) 724 (517) 724 70 724 160 724 563 724

15.1% NPV

Scenarios NPV > 0

(653) 1,702 227 (653) (564) 23 112 515

4245 0 0 1 1 1

701 702 703 704 705 706

2,191 1,630 2,297 1,749 1,754 1,319

156 156 156 156 156 156

234 239 226 222 248 281

407 396 409 398 398 389

389 207 427 246 240 122

696 323 770 418 404 62

724 724 724 724 724 724

648 275 722 370 356 14

1 1 1 1 1 1

4,996 4,997 4,998 4,999 5,000

1,395 992 1,550 832 446

156 156 156 156 156

208 219 229 208 160

391 383 394 380 372

151 79 178 26 3

181 (153) 284 (246) (554)

724 724 724 724 724

133 (201) 236 (294) (602)

1 0 1 0 0

250

200

Frequency

150

100

50

0

NPV (MM US$)

Figure 7 – First Set of Monte Carlo Simulations – NPV Histogram for 5,000 Scenarios

Table 10 – Monte Carlo Simulation – Summary – 5,000 Scenarios ( FCI-BR min ) Scenario Items Minimum Maximum Mean

Risk NPV < 0

DISCOUNTED CASH FLOW FOR EACH SIMULATION ( MM US$ ) Gross Revenue 397 3.813 1.566

Fixed Cost 126 126 126

Variable Cost 160 311 239

Other Expenses 282 350 306

Taxes 1 989 241

34

Net Income -425 1.873 406

Fixed Capital Investment 579 579 579

6,3% NPV -528 1.771 304

Scenarios NPV > 0 4684

ACCEPTED MANUSCRIPT

Table 11 – Monte Carlo Simulation – Summary – 5,000 Scenarios ( FCI-BR max ) Scenario Items Minimum Maximum Mean

Risk NPV < 0

DISCOUNTED CASH FLOW FOR EACH SIMULATION ( MM US$ ) Gross Revenue 397 3.813 1.566

Fixed Cost 200 200 200

Variable Cost 160 311 239

Other Expenses 490 558 513

Taxes 0 851 138

Net Income -861 1.575 73

Fixed Capital Investment 941 941 941

43,6% NPV -879 1.556 55

Scenarios NPV > 0 2822

Table 12 – Third Set of Monte Carlo Simulations – Summary – 5,000 Scenarios Scenario Items Minimum Maximum Mean 1 2 3 4 5

Risk

DISCOUNTED CASH FLOW FOR EACH SIMULATION ( MM US$ ) Gross Revenue 1,393 1,598 1,476 1,395 1,469 1,483 1,462 1,444

Fixed Cost 156 156 156 156 156 156 156 156

Variable Cost 160 311 239 165 243 247 236 216

Other Expenses 333 337 335 333 335 335 334 334

Net Income

Taxes

NPV

165 304 176 171 166 177 166 167

260 262 261 261 261 261 261 261

-1.0 1.0 0.1 -0.3 -0.3 -0.5 -0.5 0.3

43.3% MESP ( US$ / Liter ) Scenarios 0.043 MESP > 0.65 2.401 2163 0.639 2.401 1 1.692 1 0.790 1 0.702 1 0.461 0

701 702 703 704 705 706

1,461 1,474 1,452 1,448 1,491 1,536

156 156 156 156 156 156

234 239 226 222 248 281

334 335 334 334 335 336

166 175 165 165 181 194

262 261 262 261 262 260

0.6 0.0 0.4 0.2 0.6 -0.8

0.389 0.582 0.391 0.520 0.544 0.763

0 0 0 0 0 1

4,996 4,997 4,998 4,999 5,000

1,457 1,483 1,455 1,438 1,393

156 156 156 156 156

208 219 229 208 160

334 335 334 334 333

189 204 166 171 174

261 260 262 260 261

0.0 -0.9 0.5 -0.7 -0.1

0.679 1.003 0.608 1.136 2.064

1 1 0 1 1

35

ACCEPTED MANUSCRIPT

350

300

250

200

150

100

50

0

36

ACCEPTED MANUSCRIPT

Economic feasibility of ethanol production through a thermochemical route is assessed Route comprises sugarcane bagasse gasification and synthesis of alcohols, in Brazil The assessment uses a traditional and a stochastic approach, which are later compared With stochastic modelling a more educated decision making can be achieved

ACCEPTED MANUSCRIPT Table 1 – Thermochemical ethanol production plant – fixed capital investment - USGC basis THERMOCHEMICAL ETHANOL - CAPITAL INVESTMENT (CAPEX) Total Investment Process Areas Feed handling and dryinga Gasification Gas cleanup Alcohol synthesis Alcohol separation Steam plant and power Cooling water and other utilities Inside Battery Limits (ISBL)b Outside Battery Limits (OSBL) Total installed cost (TIC) Other direct costs Land (not depreciated) Site development (% of ISBL) 4.0% Total direct cost (TDC) Indirect costs (% of TDC ex Land) Prorated expenses 10.0% Home office and construction fees 20.0% Field expenses 10.0% Project contingency 10.0% Other cost (start-up and permits) 10.0% Fixed capital investment (FCI) a Capital cost included in feedstock price. b Battery limits exclude "Feed handling and drying" and "Gasification" Areas.

Installed Costs USA (US$)

43,250,000 55,430,000 122,050,000 20,310,000 45,840,000 9,560,000 241,040,000 55,400,000 296,440,000 1,610,000 9,641,600 307,691,600 183,648,960 30,608,160 61,216,320 30,608,160 30,608,160 30,608,160 491,340,560

ACCEPTED MANUSCRIPT Table 2 – Thermochemical ethanol production plant – annual gross and net revenues THERMOCHEMICAL ETHANOL - ANNUAL GROSS AND NET REVENUES

Scenario

Unique

Items

Unit

OPERATIONAL PHASE 1rst Quarter

Year 2 2nd Quarter 3rd Quarter

4th Quarter

Annual Gross Revenue

182,441,509

Anhydrous Ethanol

160,275,915

Quantity Price

Liters US$ / Liter

61,644,583 0.6500

61,644,583 0.6500

61,644,583 0.6500

Higher Alcohols Quantity Price ( - ) Taxes Taxes ( = ) Annual Net Revenue

61,644,583 0.6500 22,165,593

Liters US$ / Liter

7,793,809 0.7110

7,793,809 0.7110

7,793,809

7,793,809

0.7110

0.7110 182,441,509

ACCEPTED MANUSCRIPT Table 3 – Thermochemical Ethanol – Prices of products, inputs and utilities Product Ethanol Higher alcohols

PRICES OF PRODUCTS Unit (US$ / Liter) (US$ / Liter)

Price 0.650 0.711

PRICES OF INPUTS Input Biomass (sugarcane bagasse) Olivine Magnesium oxide (MgO) Tar reformer catalyst Alcohol synthesis catalyst Solids disposal Diesel fuel Boyler feed water chemicals Cooling tower chemicals Caustic soda LO-CAT chemicals DEPG makeup Amine makeup

Utility Water makeup Water treatment

Unit 2007 US$ / dry ton 2007 US$ / ton 2007 US$ / ton 2007 US$ / Kg 2007 US$ / Kg 2007 US$ / ton 2007 US$ / Liter 2007 US$ / Kg 2007 US$ / Kg 2007 US$ / dry ton US$ / Liter of ethanol produced US$ / Liter of ethanol produced US$ / Liter of ethanol produced PRICES OF UTILITIES Unit 2007 US$ / ton 2007 US$ / ton

Price 35.00 224.23 473.36 39.11 65.48 49.22 0.76 5.00 2.99 143.72 0.000247 0.000138 0.000028

Price 0.28 0.68

ACCEPTED MANUSCRIPT Table 4 – Thermochemical ethanol – Production plant annual variable operating cost

THERMOCHEMICAL ETHANOL - ANNUAL VARIABLE OPERATING COST

Scenario

Unique

Items

Unit

OPERATIONAL PHASE Year 2 1rst Quarter

2nd Quarter

3rd Quarter

4th Quarter

Annual Variable Operating Cost

32,793,033

Raw Materials and Inputs Biomass (sugarcane bagasse) Quantity Price

32,536,949 24,529,167 dry ton US$ / dry ton

175,208

175,208

175,208

175,208

35.00

35.00

35.00

35.00

Olivine Quantity Price

459,848 ton 513 513 513 513 US$ / ton 224.23 224.23 224.23 224.23

Magnesium oxide (MgO) Quantity Price

12,640 ton 7 US$ / ton

7 473.36

7 473.36

7 473.36

Tar reformer catalyst Quantity Price

1,790,321 Kg 11,444 11,444 11,444 11,444 US$ / Kg 39.11 39.11 39.11 39.11

Alcohol synthesis catalyst Quantity Price

4,845,874 Kg 18,501 18,501 18,501 18,501 US$ / Kg 65.48 65.48 65.48 65.48

Solids disposal Quantity Price

458,135 ton 2,327 2,327 2,327 2,327 US$ / ton 49.22 49.22 49.22 49.22

Diesel fuel Quantity Price Boyler feed water chemicals Quantity Price Cooling tower chemicals

473.36

240,404 Liter 79,548 79,548 79,548 79,548 US$ / Liter 0.76 0.76 0.76 0.76 62,943 Kg 3,147 US$ / Kg

3,147 5.00

3,147 5.00

3,147 5.00

5.00 13,687

ACCEPTED MANUSCRIPT Quantity Price

Kg 1,144 US$ / Kg

1,144 2.99

1,144 2.99

1,144 2.99

Caustic soda Quantity Price LO-CAT chemicals Quantity Price DEPG makeup Quantity Price Amine makeup Quantity Price

2.99 21,930

dry ton 38 US$ / dry ton

38 143.72

38 143.72

38 143.72

143.72 61,000

ton 61,644,583 61,644,583 61,644,583 61,644,583 US$ / ton 0.00025 0.00025 0.00025 0.00025 34,000 ton 61,644,583 61,644,583 61,644,583 61,644,583 US$ / ton 0.00014 0.00014 0.00014 0.00014 7,000 ton 61,644,583 61,644,583 61,644,583 61,644,583 US$ / ton 0.00003 0.00003 0.00003 0.00003

Utilities

256,084

Water makeup

180,975

Quantity Price Water treatment Quantity Price

ton 159,669 159,669 159,669 159,669 US$ / ton 0.28 0.28 0.28 0.28 75,109 ton 27,604 27,604 27,604 27,604 US$ / ton 0.68 0.68 0.68 0.68

ACCEPTED MANUSCRIPT Table 5 – Thermochemical ethanol – Production plant annual labor cost THERMOCHEMICAL ETHANOL - ANNUAL LABOR COST Total Labor Cost (US$ / year) 1,014,128 Position Title Salary (2007 US$) Positions Total Cost (2007 US$) Administration 169,583 Plant manager 147,000 1 147,000 Clerks and secretaries 7,528 3 22,583 Production 774,288 Plant engineer 70,000 1 70,000 Maintenance supervisor 57,000 1 57,000 Laboratory manager 56,000 1 56,000 Shift supervisor 48,000 5 240,000 Lab technician 8,364 2 16,728 Maintenance technician 8,364 16 133,824 Shift operators 10,037 20 200,736 Other services 70,258 Yard employees 5,855 12 70,258

ACCEPTED MANUSCRIPT Table 6 – Thermochemical ethanol – Production plant annual fixed operating cost THERMOCHEMICAL ETHANOL - ANNUAL FIXED OPERATING COST

Scenario Items

Unit

Annual Fixed Operating Cost

Year 2

1rst Quarter

2nd Quarter

3rd Quarter

4th Quarter 34,258,415

Labor Cost Labor cost - Operation (4 shifts) Labor cost - Maintenance (1,6% ISBL

OPERATIONAL PHASE

Unique

1,014,128 US$

p.y.)

US$

Labor cost - Laboratory (20% Labor Operation.)

US$

Other fixed cost

33,244,286

Benefits and general (90% Labor Cost)

US$

912,716

Maintenance (3,0% FCI-BR p.y.)

US$

26,214,787

Insurance and taxes (0,7% FCI-BR p.y.)

US$

6,116,784

ACCEPTED Table 7 – Thermochemical ethanol – Discounted cash flowMANUSCRIPT – Summary THERMOCHEMICAL ETHANOL - DISCOUNTED CASH FLOW - SUMMARY Items Present Value Present Value Gross Revenue Domestic Sales Foreign Sales ( - ) Taxes ( = ) Net Revenue

Case 1: Market Price

Case 2: Floating Price

1,367,632,734 1,367,632,734

1,467,159,432 1,467,159,432

1,367,632,734

1,467,159,432

Total Cost ( - ) Fixed Cost ( - ) Variable Cost

403,184,364 155,906,627 247,277,737

( = ) EBITDA ( - ) Depreciation

964,448,370

1,063,975,068 308,988,699

( = ) Operating Profits

655,459,671

754,986,369

Total Expenditures ( - ) Comercial Expenses ( - ) Administrative Expenses ( - ) Financial Expenses (Interests)

332,569,701 27,352,655

334,560,234 29,343,189

( = ) EBT ( - ) Taxes

322,889,971 130,069,808

( = ) Net Income ( + ) Depreciation ( - ) Fixed Capital Investment ( - ) Capacity Maintenance ( + ) Loan ( - ) Loan Amortization (+/-) Working Capital ( + ) Residual Value

192,820,163

( = ) Net Present Value (NPV)

-64,373,868

103,937,752 201,279,294 420,426,135 163,232,103 257,194,031 308,988,699 723,716,049 526,019,377 246,871,090 -29,825,220

Discount Rate

0 9.0%

Internal Rate of Return (IRR)

7.6%

9.0%

Ethanol Selling Price (ESP)

0.650

0.704

ACCEPTED Table 8 – Stochastic Modelling Parameters

MANUSCRIPT

MRP Parameters

(2007 US$)

(2007 US$)

Sugarcane Bagasse Anhydrous Ethanol Higher Alcohols

35.00 0.650 0.497

36.0754 1.0101 1.5906

 1.21737 0.31491 0.05645

σ 49.247% 33.507% 32.106%

ACCEPTED MANUSCRIPT Table 9 – First Monte Carlo Simulation – Summary – 5,000 Scenarios

Scenario

Risk NPV <0

DISCOUNTED CASH FLOW FOR EACH SIMULATION (MM US$)

15.1% Other Gross Fixed Variable Net Fixed Capital Items Taxes NPV Expenses Revenue Cost Cost Income Investment Scenarios Minimum 397 156 160 371 1 -605 724 -653 NPV > 0 Maximum 3,813 156 311 439 932 1,750 724 1,702 4,245 Mean 1,566 156 239 394 193 274 724 227 1 397 156 165 371 1 -605 724 -653 0 2 568 156 243 374 3 -517 724 -564 0 3 1,260 156 247 388 90 70 724 23 1 4 1,371 156 236 390 119 160 724 112 1 5 1,967 156 216 402 321 563 724 515 1 701 702 703 704 705 706

2,191 1,630 2,297 1,749 1,754 1,319

156 156 156 156 156 156

234 239 226 222 248 281

407 396 409 398 398 389

389 207 427 246 240 122

696 323 770 418 404 62

724 724 724 724 724 724

648 275 722 370 356 14

1 1 1 1 1 1

4,996 4,997 4,998 4,999 5,000

1,395 992 1,550 832 446

156 156 156 156 156

208 219 229 208 160

391 383 394 380 372

151 79 178 26 3

181 -153 284 -246 -554

724 724 724 724 724

133 -201 236 -294 -602

1 0 1 0 0

ACCEPTED MANUSCRIPT Table 10 – Monte Carlo Simulation – Summary – 5,000 Scenarios (FCI-BR min) Scenario

Items

Risk NPV <0

DISCOUNTED CASH FLOW FOR EACH SIMULATION (MM US$) Fixed Variable Other Net Taxes Expenses Revenue Cost Cost Income Gross

Minimum Maximum

397 3,813

126 126

160 311

282 350

1 989

-425 1,873

Mean

1,566

126

239

306

241

406

6.3% Fixed Capital Investment

NPV

579 -528 579 1,771 579

304

Scenarios NPV > 0 4,684

ACCEPTED MANUSCRIPT Table 11 – Monte Carlo Simulation – Summary – 5,000 Scenarios (FCI-BR max) Scenario

DISCOUNTED CASH FLOW FOR EACH SIMULATION (MM US$)

Risk NPV <0

43.6% Fixed Other Gross Variable Net Fixed Capital Items Taxes NPV Expenses Revenue Cost Cost Income Investment Scenarios Minimum 397 200 160 490 0 -861 941 -879 NPV > 0 Maximum 3,813 200 311 558 851 1,575 941 1,556 2,822 Mean 1,566 200 239 513 138 73 941 55

ACCEPTED MANUSCRIPT Table 12 – Third Set of Monte Carlo Simulations – Summary – 5,000 Scenarios Scenario Items

Risk

DISCOUNTED CASH FLOW FOR EACH SIMULATION (MM US$) Fixed Variable Gross Revenue Cost Cost

Other Net Taxes NPV Expenses Income -1.0 1.0 0.1 -0.3 -0.3 -0.5 -0.5 0.3

MESP (US$ / Liter)

43.3%

Scenarios MESP > 0.043 0.65 2.401 2,163 0.639 2.401 1 1.692 1 0.790 1 0.702 1 0.461 0

Minimum Maximum Mean 1 2 3 4 5

1,393 1,598 1,476 1,395 1,469 1,483 1,462 1,444

156 156 156 156 156 156 156 156

160 311 239 165 243 247 236 216

333 337 335 333 335 335 334 334

165 304 176 171 166 177 166 167

260 262 261 261 261 261 261 261

701 702 703 704 705 706

1,461 1,474 1,452 1,448 1,491 1,536

156 156 156 156 156 156

234 239 226 222 248 281

334 335 334 334 335 336

166 175 165 165 181 194

262 0.6 261 0.0 262 0.4 261 0.2 262 0.6 260 -0.8

0.389 0.582 0.391 0.520 0.544 0.763

0 0 0 0 0 1

4,996 4,997 4,998 4,999 5,000

1,457 1,483 1,455 1,438 1,393

156 156 156 156 156

208 219 229 208 160

334 335 334 334 333

189 204 166 171 174

261 0.0 260 -0.9 262 0.5 260 -0.7 261 -0.1

0.679 1.003 0.608 1.136 2.064

1 1 0 1 1