A Colombian Case for Conversion of Sugarcane Bagasse to Ethanol

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SELECTION OF PROCESS PATHWAYS FOR BIOREFINERIES DESIGN USING OPTIMIZATION TOOLS. A COLOMBIAN CASE FOR CONVERSION OF SUGARCANE BAGASSE TO ETHANOL, PHB AND ENERGY. Jonathan Moncada, Luis Geronimo Matallana, and Carlos Ariel Cardona Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/ie3019214 • Publication Date (Web): 18 Feb 2013 Downloaded from http://pubs.acs.org on February 19, 2013

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Industrial & Engineering Chemistry Research

SELECTION OF PROCESS PATHWAYS FOR BIOREFINERIES DESIGN USING OPTIMIZATION TOOLS. A COLOMBIAN CASE FOR CONVERSION OF SUGARCANE BAGASSE TO ETHANOL, PHB AND ENERGY. Jonathan Moncada a, Luis G. Matallana a, Carlos A. Cardona. a,1 [email protected], [email protected], [email protected] a

Instituto de Biotecnología y Agroindustria, Departamento de Ingeniería Química. Universidad Nacional de Colombia sede Manizales. Cra. 27 No. 64-60, Manizales, Colombia.

Keywords. Optimization, Biorefinery, Sugarcane bagasse, Bioenergy, Biomaterials.

1

Corresponding author Tel.: +57 6 8879300x50417; fax: +57 6 8859300x50199. E-mail: [email protected] (C.A. Cardona).

ACS Paragon Plus Environment

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ABSTRACT

In this paper a techno-economic and environmental analysis for a sugarcane bagasse biorefinery in Colombia case is presented. It is shown an optimization procedure in order to select the most promising process pathway for the production of fuel ethanol, PHB and electricity. Once the optimization procedure was done, its results serve as criteria for the selection of technologies and raw materials distribution. The distribution results and technologies were used to feed the knowledge-based approach in process synthesis. Then three scenarios were simulated using the Aspen Plus software. The first scenario consists in energy cogeneration, the second one is an arbitrary distribution and the third one corresponds to the pre-selected pathway using the optimization subroutine. Each scenario was assessed from the techno-economic and environmental point of view according to the Colombian conditions. The best to the configuration previously designed through the optimization subroutine.

For this case, the

obtained economic margin was 53.83%, the potential environmental impact to 0.16 PEI/kg products and the biological GHG emissions of the processing stage represented as kg of biological CO2-e/kg of bagasse to 1.55.

1. INTRODUCTION Sugarcane is one of the most important energy crops in tropical countries like Brazil and India, especially to produce fuel ethanol 1. Colombian sugarcane is the main feedstock for the production of sugar as both food and food additive and fuel ethanol as indirect energy for the Colombian gasoline oxygenation program 1. Recently electricity and steam as direct energy are produced through cogeneration schemes from sugarcane bagasse. This energy is generally used in the different production processes of cane (e.g. Sugar production, Ethanol production),


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nevertheless, the electricity surplus is commercialized 1. On the other hand, the Colombian government and sugarcane sector looks forward to increase productivity, using waste materials and residues in a reasonable way. This, is considered in order to analyze different possibilities to obtain new products, making investments in research for integrated process flowsheets and new technologies 1. For this reason, sugarcane bagasse becomes a very important feedstock to be taken into account in the production of different chemicals and high added value products.

On the other hand, the biorefinery concept is adopted in many sectors for the integrated production of biofuels, bioenergy, biomaterials, biomolecules and food products 2-7. Considering this and the mentioned about the current usage and perspectives of sugarcane bagasse in Colombia, the inclusion of a second generation biorefinery based on this feedstock (sugarcane bagasse) leads to the formulation and evaluation of different biorefining alternatives for obtaining different and new products. In this way, the design and evaluation of biorefineries involves open and complex problem.

The latter is due to the competition with conventional processes in order to achieve maximum efficiencies 3, 4, 8. Considering the mentioned previously, the design and synthesis of biorefineries can be faced using different approaches. This is why the computer aided process engineering is considered as a very important tool which can be used to support the conceptual design of biorefineries and also can be used to address specific technical barriers 9-12 8, 13. As mentioned by Bao et al. (2011)

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, to synthesize a cost-effective biorefinery various technologies should be

examined and analyzed. In this way the selection of different process pathways in a multiproduct integrated biorefinery becomes a very important subject of study as previously discussed by


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some authors 3, 7, 9, 10, 14-24. For instance, Ng et al. (2009) 15 proposed a hierarchical procedure for the synthesis of different potential pathways for integrated biorefineries. Based on a systematic approach, it was possible to screen competing technologies based on feasibility and gross revenue criteria. On the other hand, Zondervan et al. (2011)

14

proposed an optimization model

that can be used to find the optimal process pathway for the production of ethanol, butanol, succinic acid and blends of this products with gasoline. Pham & El-Halwagi (2011) 16 proposed a two stage approach for the synthesis and optimization of biorefinery configurations. The method referred as "forward-backward", which involves forward synthesis of biomass to possible intermediates and reverse synthesis, starting with the desired products to identify the species and pathways. Ponce-Ortega et al. (2012) 19 proposed a general systematic approach for the design of optimal pathways in a biorefinery. This approach allows the identification of optimal biorefinery configurations for different criteria (e.g. techno-economic, environmental). Bao et al. (2011)

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introduced a shortcut method for the synthesis and screening of integrated biorefineries. This method is useful to evaluate potential configurations of biorefineries using simple data for the solution of the optimization/superstructure model. Nevertheless, one of the characteristics of the procedures and methods shown above is the complexity in the formulation of the optimization problems. This is basically due to the modeling criteria that is included in the formulation (e.g. detailed economic assessment, environmental constraints). Otherwise, many different approaches for solving the process pathways problem can be adopted. However, it is very important to contextualize the procedures to different regions (i.e Colombia).

Considering the latter, the formulation of simpler models, which can be used as the basis of a complex assessment, could be an interesting option depending on the design approach. Thereby,


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it could be analyzed the possibility of coupling an optimization approach with a classical approach (i.e. onion diagram) in the conceptual design. Considering this, the main tool shown in this paper is the screening of different technologies and pathways using a simple optimization model. The results of the optimization model serve as a preliminary result, which address the decision making in terms of feedstock distributions and logical technological sequences. Then, a process simulation procedure is done using the knowledge-based approach for process synthesis 25, 26

. Given this, the optimization approach constantly retrofits the knowledge-based approach. In

this sense, the aim of this paper is to determine the most promising process pathway for a sugarcane bagasse based biorefinery in the Colombian context. This is considered in order to produce in the same facility fuel ethanol, Poly-3-hydroxybutyrate (PHB) and energy (electricity and heat). This procedure is done using an optimization formulation. Then the most promising pathway is compared with energy cogeneration from bagasse (current use) and a pathway selected in an arbitrary way for the production of ethanol, PHB and electricity. The comparison is done from the technical, economic and environmental point of view for the processing facility.

The remains of this paper is shown as follows. Section 2 explains the methodology in pathway selection considering the technological schemes, the formulation of the optimization problem and the simulation procedure. Section 3 includes results and discussion where a comparison of different scenarios is shown from both techno-economic and environmental points of view. The conclusions section closes the paper.


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2. METHODOLOGY 2.1. Pathway Selection The methodology for the selection of the most promising process pathway consists on the identification of a set of possible routes in which feedstocks are transformed into products. The selection is based on different criteria such as conversion efficiency and energy consumption, which directly affect the total production cost. Then, the selection begins with the formulation of the optimization problem. However, it is very important to establish the desired products and the fundament of selecting them. Considering the latter, three products from cane bagasse were taken into account as an alternative to the current use of cane bagasse in sugar millings (i.e. cogeneration systems). The selected products are fuel ethanol, PHB and Energy. Fuel ethanol was selected considering that a contribution to the national oxygenation program is expected using second-generation feedstocks. Another reason for selecting fuel ethanol as a product, is the capacity to be produced from fermentable sugars as a result of cellulose and hemicellulose hydrolysis (i.e. pentose, hexoses)

27-30

. The inclusion of PHB as a new product is promising

because this biopolymer is produced mainly from sugars. In this case glucose can be used for PHB synthesis as the carbon source. However, the selection of the PHB as a product in the Colombian context is based on the necessity to include biopolymers in the market due to its increasing consumption and a fast increment on oil prices. Therefore, this biopolymer (PHB) represents an alternative to the current oil based plastics. Nevertheless, the production cost of PHB is very high compared with oil based polymers. Considering this, some authors reported that the total production cost of PHB depends mainly on the carbon source price and utilities, that are generally responsible of a large percentage of the total production cost 31, 32. In this way, the evaluation of PHB production as a line integrated into a biorefinery could lead to the


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reduction of production costs. The latter is mainly because some streams are coupled in the same facility or different processes could share the material flows between each other 2. Therefore, transportation cost, as well as, other costs associated to the supply chain will be avoided if the production is done in the same facility 2. Finally, it is important to conserve electricity production from cane bagasse together with fuel ethanol and PHB production, not only to contribute with energy requirements of the same plant (i.e. heat and power) but also to commercialize the surplus electricity.

On the other hand, many different technologies can be used to obtain the selected products from cane bagasse. However, it was included key technologies that previously were analyzed in different works

33-36 37, 38

. In this way, the structure of a sugarcane bagasse biorefinery for the

integrated production of PHB, Ethanol and Energy is shown in figure 1. This figure includes sugarcane bagasse main components and intermediate products that are raw materials for other processes. Technologies are described with numbers with its corresponding explanation. At this point it is very important to note that the split of the main components of cane bagasse is the result of a technological sequence. The first step is the pretreatment, which generally fractionates hemicellulose into pentose (e.g. xylose). Then the solid fraction (mainly cellulose and lignin) that is not converted into pentose in the pretreatment stage is sent to the cellulose hydrolysis section. The cellulose hydrolysis results in a solid fraction (mainly lignin) and liquid fraction (e.g. hexoses). Finally xylose, glucose and lignin are used as raw material for the transformation into the final products. In order to give an idea of the sugarcane bagasse composition, table 1 shows the contribution of cellulose, hemicellulose and lignin in cane bagasse from Colombia.


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Technologies. (1) Dilute Acid Pretreatment (2) Dilute Acid Hydrolysis (3) Enzymatic Hydrolysis (4) Ethanol Production. Xylose fermentation by recombinant Zymomonas Mobilis (5) Ethanol Production. Glucose fermentation by recombinant Zymomonas Mobilis (6) Ethanol Production. Glucose fermentation by recombinant Saccharomyces Cerevisiae. (7) PHB Production. Glucose fermentation by Cupriavidus Necator (8) Combined cycled-biomass gasification. (9) Combined cycled-biomass gasification.

Figure 1: Sugarcane bagasse biorefinery structure for PHB, fuel Ethanol and Electricity production Table 1. Sugarcane bagasse main composition 39. Percentage by weigth (%) Lignin 32.91 Cellulose 37.65 Hemicellulose 29.44 Component

The necessary data to be fed in the optimization model is basically summarized in processing yields and energy requirements for the different technologies shown in figure 1. In order to evaluate the technologies presented, kinetic models and individual technology simulations were used as the basis for the calculation of the required data. For instance the kinetic model used for the pretreatment and dilute acid cellulose hydrolysis steps were reported by Jin et al., 2010

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.

The kinetic model for enzymatic cellulose hydrolysis was reported by Morales-Rodriguez et al., 2011

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. The kinetic model for ethanol production using recombinant Zymomonas Mobilis was

reported by Leksawasdi et al., 200137. The kinetic model used for the calculation of PHB


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production was reported by Shahhosseini, 2004

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. The biomass gasification coupled with a

combined cycle was simulated using the Aspen Plus software. This procedure was done for technologies (8) and (9) (see figure 1) using lignin and cane bagasse respectively. It is very important to mention that the calculation of energy requirements was done using the Aspen Plus software for each technology shown in figure 1. These requirements includes different equipment as heat exchangers important in the adequacy of up-streams and down-streams of each technology. Nevertheless, the complete downstream processing for each technology was not considered in the optimization formulation. At this point it is evident the complete interaction between the hierarchical decomposition approach and the optimization approach.

Once the yields were determined, the data were used in the material and energy balances in the formulation of the optimization problem. In this way, table 2 summarizes the yields for each of the proposed technologies. On the other hand, each technological pathway has its own energy requirements; therefore a cost is related to each one. This energy costs refer to the utilities needed to cover the supply. Therefore, the results of the energy requirements were used to formulate the economic objective in the optimization problem. Considering this, table 3 shows the energy requirements per unit of product or intermediate product for each of the technologies. Another important aspect to consider is the utility prices per mass unit, as well as, raw materials costs, therefore table 4 shows the prices used in the formulation of the objective function.


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Table 2. Technology yields of the desired products. Technology 1 2 3 4 5 6 7 8 9

Yield 0.74 0.89 0.75 0.48 0.47 0.49 0.31 5108.78 5696.94

Units g xylose/g hemicellulose g glucose/g cellulose g glucose/g cellulose g EtOH/g xylose g EtOH/g glucose g EtOH/g glucose g PHB/g glucose MJ/tonne dry lignin MJ/tonne dry baggase

Table 3. Technology energy needs and energy costs. Technology 1 2 3 4 5 6 7

Energy needs Heating Cooling 8537.12 0.00 4389.06 2936.25 2194.53 587.25 143238.45 57306.17 71949.45 28785.20 69012.74 27610.29 44010.42 27184.93

Energy Costs (USD/MJ) Heating Cooling 6.30*10-04 7.85*10-05 6.30*10-04 7.85*10-05 6.30*10-04 7.85*10-05 7.68*10-04 7.85*10-05 7.68*10-04 7.85*10-05 7.68*10-04 7.85*10-05 6.30*10-04 7.85*10-05

Units MJ/tonne xylose MJ/tonne glucose MJ/tonne glucose MJ/Tonne Ethanol MJ/Tonne Ethanol MJ/Tonne Ethanol MJ/Tonne PHB

Table 4. Prices used in the economic analysis. Item Sugarcane bagasse Fuel Ethanol PHB Electricity High P. steam (105 bar) Mid P. steam (30 bar) Low P. steam (3 bar) Water a

Unit

Price

Reference

USD/tonne

35 a

1

USD/L USD/kg USD/kWh

1.24 3.12 0.1

40

USD/tonne

9.86

29

USD/tonne

8.18

29

USD/tonne

1.57

29

USD/m3

1.252

29

32 41, 42

Price due to transport charges. Distance traveled approx. 120 km. Type of truck: 10 Tonne Truck. Diesel price: 4.11 USD/gallon


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Considering the aspects described above, the optimization problem consists on the identification of the most promising process pathway, which includes the minor energy consumption. The optimization problem looks forward to an economic margin/potential maximization, which is defined as the difference between product sales and production cost from raw materials and energy requirements. Accounting this, the formulation of the optimization problem is shown below: n

n

m

i

i

i

max Z = ∑ mi pi +∑ wk pk − mbagasse pbagasse − ∑ m j p j

s.t. mi , m j , mbagasse , wk ≥ 0

(1) (2)

Where mi is the mass of the products (PHB, ethanol), shown in Figure 1. pi Is the sale price for each one. wk is the generated work and pk its sale price. m j is the mass of components that are the basis in energy cost calculation as shown in Table 3 for each technology, and p j is the cost associated with energy consumption for the proposed sequence as shown in Table 3. mbagasse corresponds to bagasse mass to be processed expressed in tonne/h and pbagasse its corresponding price. Then, Z corresponds to the objective function to be maximized, in this case the economic margin. Also the global mass balance is considered in the optimization problem. The other equations to take into account in the model with its respective explanation are shown as follows:

mbagasse = mbagasse,1−8 + mbagasse,9

(3)

mCellulose = mbagasse,1−8 ⋅ 0.3765

(4)

mHemicellulose = mbagasse,1−8 ⋅ 0.2944

(5)

mLignin = mbagasse,1−8 ⋅ 0.3291

(6)


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Equation (3) indicates the mass of bagasse that is directed to pretreatment section (technology 1). This mass is indicated with the subscript 1-8. The mass of bagasse indicated with subscript 9 goes directly to the cogeneration system to produce electricity (technology 9). Equations 4 to 6 indicate the mass balance corresponding to the bagasse global composition shown in table 1. Because the first stage in the transformation corresponds to the pretreatment stage (technology 1), equation 7 reflects the material balance for hemicellulose treatment, which leads to the generation of xylose and a solid fraction that needs to be sent to cellulose hydrolysis (technologies 2 and 3). At this point it is very important to note that the script y corresponds to the technology yield and subscript number to its corresponding technology represented in Fig 1 and Table 2.

mxylose,1 = y1 ⋅ mHemicellulose

(7)

On the other hand, the processing stage that follows the pretreatment is the cellulose hydrolysis. In this stage it is very important to select one of the two technologies available for the analysis. Considering this, it is very important to propose the correct disjunction to select the appropriated technology. Then the formulation of a mixed integer programming (MIP) problem is necessary

43-48

. For this development using binary variables ( X i ) and big-M constraints, the

problem corresponds to a big-M MIP formulation 45, 48.

mCellulose,2 ≤ M 2 ⋅ X 2

(8)

mCellulose,3 ≤ M 3 ⋅ X 3

(9)

X2 + X3 =1

(10)

mCellulose = mCellulose,2 + mCellulose,3

(11)

In this model, Mi are the ‘big-M’ parameters that render the inequalities of equations 8 and 9 redundant when Xi=0. In the same way, mass balance always has to be satisfied, in this case for


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cellulose composition as shown in equation 10. Equations 12-14 show the mass balance for glucose production in technologies 2 and 3. The method is capable to select the most appropriated of the two technologies with the formulation described for cellulose. Once the cellulose stage is done, the lignin fraction is used to feed the cogeneration system (technology 8). Also a fraction of cane bagasse is considered to feed directly the cogeneration system (see eq. 3). At this point no disjunction is considered because lignin and bagasse contribute to the electric power generation (eq. 15).

mglucose,2 = y2 ⋅ mCellulose,2

(12)

mglucose,3 = y3 ⋅ mCellulose,3

(13)

mglucose = mglucose,2 + mglucose,3

(14)

wElectricity = y8 ⋅ mLignin + y9 ⋅ mbagasse,9

(15)

As described in Fig. 1, PHB and fuel ethanol can be produced from the same carbon source (glucose). In this way, the optimization problem includes which can be the possible distribution, defined as percentage of glucose going to PHB and Ethanol Production. Also the model includes which is the bagasse fraction (or percentage) going to treatment and the fraction going to cogeneration. The latter is one of the most difficult results to obtain in a biorefinery. This is basically due to the enormous possibilities and criteria that can define the distribution. This is not always easy to get in a simple view. Nevertheless, the most important added value of the optimization model is the simple data required for the analysis and the importance to be retrofitted constantly with the knowledge-based approach.

Considering the mentioned

previously, eq. 16 considers the mass of glucose going to ethanol production (subscripts 5 and 6, because there are two possibilities) and the mass of glucose that is used in the PHB production (subscript 7).


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mglucose = mglucose,5−6 + mglucose,7

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(16)

In the case of ethanol production from glucose two possible technologies were considered (different fermentative microorganism). Then, the formulation is similar to that shown in technologies 2 and 3. Therefore a "big M" formulation is needed in order to select the most promising technology (eqs. 17-20). The PHB production is represented in eq. 21.

mglucose,5 ≤ M 5 ⋅ X 5

(17)

mglucose,6 ≤ M 6 ⋅ X 6

(18)

X5 + X6 =1

(19)

mglucose,5−6 = mglucose,5 + mglucose,6

(20)

mPHB = mglucose,7 ⋅ y7

(21)

Equations 22 to 25 show the mass balance for ethanol production in technologies 4 to 6 (also considering xylose fermentation). Finally equations 26 to 28 consider the restrictions of the optimization problem. Here is important to emphasized that sugarcane bagasse flowrate is an optimization variable that is calculated depending on the goals on productivity of the biorefinery (restrictions). Nevertheless, the model will also work if a fixed biomass capacity is considered. On the other hand it is very important to note that a simple change as the biomass flowrate can also drive to a different solution.

mEthanol ,4 = mxylose,4 ⋅ y4

(22)

mEthanol ,5 = mglucose,5 ⋅ y5

(23)

mEthanol ,6 = mglucose,6 ⋅ y6

(24)


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mEthanol = mEthanol ,4 + mEthanol ,5 + mEthanol ,6

(25)

Productivity restrictions: PHB production: lower 25 tonne/day. upper 40 tonne/day. Data expressed in tonne/hr in the model for unit consistency. 1.04 ≤ mPHB ( tonne h ) ≤ 1.67

(26)

Ethanol production: lower 160000 L/day. upper 300000 L/day. Data expressed in tonne/hr in the model for unit consistency. 5.36 ≤ mEthanol ( tonne h ) ≤ 10.05

(27)

Electricity production: lower 40 MW. upper 55 MW. Data expressed in MJ/hr in the model for unit consistency. 144,000 ≤ wElectricity ( MJ h ) ≤ 198,000

(28)

These restrictions take into account which could be the plant capacity that contributes significantly in different Colombian goals or expected market. For instance, the selection of ethanol plant capacity depends exclusively on the fuel ethanol volume produced per day. The majority of ethanol plants in Colombia have installed capacities between 100,000 to 350,000 L/day. Consequently the restriction in fuel ethanol production between 160,000 to 300,000 L/day is a representative ethanol capacity to contribute directly in the Colombian Oxygenation Program. This is very important because second generation ethanol is expected in future scenarios in order to cover the projected ethanol blending 40. In the case of electricity production, the most important aspect that accounted in the selection of the restrictions is the possibility to produce power at industrial scale 1. Another aspect to consider in this selection is the projections of the sugarcane sector in Colombia to generate 300 MW of electric power in 2014 1, then the capacity between 40 and 55 MW contributes in a significantly way. The production of PHB is a


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projected view in order to produce 25 to 40 tonne/day, specifically to take into account a biopolymer in the Colombian market. This also serves as a basis to draw recommendations about the production of high-added products from a sugar based platform (e.g. hexoses from cellulose).

Once the solution of the optimization problem is obtained, raw materials distribution (previously defined) and the most promising pathway are used to feed a simulation procedure done in Aspen Plus software. Then, the most promising pathway is compared with a cogeneration scheme to produce heat and power, and an arbitrary distribution for the integrated production of PHB, ethanol and energy. This leads to the comparison of three different scenarios. The main purpose of this procedure is to show that an optimized pathway has a better economic potential than other pathways that were not optimized. Nevertheless a sensitive analysis was considered in order to identify key parameters and their effect on optimization results. In this way it was considered that the most representative parameters to be varied were both PHB price and bagasse flowrate. PHB price was varied since the current price of this biopolymer in the international market is relatively high (around 3.12 USD/kg). In this way, the effect of reducing its selling price in a 50% was evaluated. This is significantly relevant because PHA’s production cost can be reduced if an integrated biorefinery approach. On the other hand, the variation of feedstock flowrate (cane bagasse), represents the possible fluctuations of feedstock availability depending on the harvested sugarcane and bagasse resulting from cane millings. The latter is significantly relevant since it affects the complete performance and distribution of the biorefinery. This is reflected in the optimization results. However, it is also important to note that other important parameters as yields and other product prices were not varied. The main reason to not vary yields was that the optimization problem is a basic start for a deeper analysis as


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mentioned previously. Consequently, the reason of not varying fuel ethanol and electricity prices is that the Colombian government continuously regulate the sale prices of this two products, then the market is most of the time stable and it is fully covered and protected by the Colombian government.

2.2. Simulation Procedure For a given scenario, flowsheet synthesis was carried out using process simulation tools. The objective of this procedure is to generate the mass and energy balances from which the requirements for raw materials, consumables, utilities and energy needs are calculated. For this, the main simulation tool used was the package Aspen Plus v7.1 (Aspen Technology, Inc., USA). Specialized packages for performing mathematical calculations were also used. For instance GAMS platform with the baron solver was used

49

to solve the optimization model. The baron

solver is able to solve both MIP and MINLP problems. Matlab software was used to solve the different kinetic models

50

. One of the most important issues to be considered during the

simulation is the appropriate selection of the thermodynamic models that describe the liquid and vapor phases. The Non-Random Two-Liquid (NRTL) thermodynamic model was applied to calculate the activity coefficients of the liquid phase and the Hayden-O’Conell equation of state was used to describe the vapor phase. Additional data on components physical properties required for simulation were obtained from the work of Wooley and Putsche (1996) 51.

2.3. Estimation of energy consumption and total costs. The estimation of the energy consumption was performed based on the results of the mass and energy balances generated by the simulation. For this, the thermal energy required in the heat


17


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exchangers, and re-boilers was taken into account, as well as the electric energy needs of the pumps, compressors, mills and other equipment. The capital and operating costs were calculated using the software Aspen Economic Analyzer (Aspen Technologies, Inc., USA). However, specific parameters regarding some Colombian conditions such as the costs of the raw materials, income tax, labor salaries, among others, were incorporated in order to calculate the production costs per unit of the different obtained products. Table 4 shows prices for utilities and main raw materials and products in sugarcane biorefinery for the Colombian context.

2.4. Environmental assessment. The Waste Reduction Algorithm WAR, developed by the National Risk Management Research Laboratory from the U.S. Environmental Protection Agency (EPA) was used as the method for the calculation of the Potential Environmental impact. This method proposes to add a reaction of conservation over the potential environmental impact (PEI) based on the impact of input and output flowrates from the process. For this application the EPA developed a software named WAR GUI. The PEI for a given mass or energy quantity could be defined as the effect that those (energy and mass) will have on the environment if they are arbitrary discharged. The environmental impact is a quantity that cannot be directly measure, however, it can be calculated from different measurable indicators

52-55

. The WAR GUI software incorporates the Waste

Reduction Algorithm in process design measuring eight categories. These categories are: Human toxicity by ingestion (HTPI), human toxicity by dermal exposition or inhalation (HTPE), terrestrial toxicity potential (TTP), aquatic toxicity potential (ATP), Global warming (GWP), Ozone depletion potential (ODP), Photochemical oxidation potential (PCOP) and acidification Potential (AP). This tool considers the impact by mass effluents and the impact by energy


18

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requirements of a chemical process, based on the energy and mass balances generated in Aspen Plus. Then the weighted sum of all impacts ends into the final impact per kg of products. It is very important to clarify that this environmental assessment only corresponds to the possible impact generated in the productive process stage. In other words, the limits of the system correspond to the processing facility. In this way a LCA is not considered, and a simpler evaluation is carried out considering the mass and heat balances from the technical evaluation. The latter also serve as a basis to compare different processing configurations.

On the other hand, other important environmental parameter is the Green House Gas (GHG) emissions associated to a chemical process. This procedure was completed following the IPPC Guidelines of 2007 56.The GHG emissions are calculated using equivalent factors of 21 for CH4 and 4.5 for CO. It is very important to note that this evaluation corresponds to the potential emissions related to the productive process stage of the supply chain. This only relates emissions associated with the mass and energy balances of the processing stage. Otherwise, considering that the biorefinery configurations were coupled with cogenerations systems the GHG emissions corresponds to the mass balance in the exhausted gases from gasification/combustion processes. This is very relevant to be mentioned, because the optimization model was configured to consider heat integration levels taking into account that the steam generated in the cogeneration systems were capable to cover all the heating targets after the integration. Also the electricity generated was used to cover facility demands, therefore the surplus were commercialized as previously mentioned. Given this, it is expected that the emissions correspond to biological emissions, and fossil emissions can be avoided following the proposed biorefinery configurations.


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3. RESULTS AND DISCUSSION 3.1. Optimization model solution One of the most important things to consider is the retrofit and the direct relation of the optimization approach with the knowledge-based design approach. As mentioned, the result that the optimization problem shows is the basis of the conceptual analysis. To make more illustrative the results of the optimization problem, Figure 2 considers the most promising pathway for the integrated production of PHB, ethanol and energy. In the same way, table 5 shows results of the optimization model. Technologies. (1) Dilute Acid Pretreatment (2) Dilute Acid Hydrolysis (4) Ethanol Production. Xylose fermentation by recombinant Zymomonas Mobilis (6) Ethanol Production. Glucose fermentation by recombinant Saccharomyces Cerevisiae. (7) PHB Production. Glucose fermentation by Cupriavidus Necator (8) Combined cycled-biomass gasification. (9) Combined cycled-biomass gasification.

Figure 2: Most promising process pathway to produce ethanol, PHB and electricity in a sugarcane bagasse based biorefinery.

As can be seen in Table 5 when a binary variable is zero, the variable that corresponds to that pathway is also zero (technologies 3 and 5). Also the results show that is reasonable to produce ethanol in separated bioreactors using xylose and glucose respectively. This leads to a very


20

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interesting design, because the general overview in second-generation ethanol refers to hexosepentose mixture fermentations

26, 30, 37, 57-61

. In this way, the possibility to produce ethanol in a

parallel bioreactor configuration (i.e. one using xylose, another using glucose), may lead to a higher productivity in terms of ethanol volume. Nevertheless, it has to be analyzed which could be the possible impact of using separated bioreactors with different strains in the separated fermentation of xylose and glucose. On the other hand, the separated fermentation of xyloseglucose served as the basis to response an interesting question. This is related to the current use of xylose (pentose) in a multiproduct biorefinery, which is generally in the ethanol production. Nevertheless, xylose can be used as a different platform to produce high added value products as the case of xylitol and so on

62-64

. Thereby, a separated production of ethanol using different

carbon sources can be useful when a biorefinery make the decision of using xylose as a new platform for other products. Given this, glucose route will not be affected by this decision and the selected strain for glucose route fermentation will be stable. Nevertheless, the global productivity of fuel ethanol will significantly decrease, but this problem can be addressed solving other question, for instance, feedstock availability. On the other hand, the results show that the productivity of fuel ethanol, PHB and electricity corresponds to the upper bounds of equations 26 to 28. However, it is very important to note that the inclusion of sugarcane bagasse flowrate as an optimization variable leads to this result. The latter is due to the objective function to be maximized and its related constraints. For instance, if an environmental constraint is added in the model for each technology shown in figure 1, the result could be completely different in terms of process pathway and distribution. On the other hand, if the raw material flowrate is limited (availability) and considered as a parameter in the model, the result could be also different (see figure 3a). At this point it is very important to note that the model includes techno-economic


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criteria. Nevertheless, new criteria can be also used to consider different important aspects in a biorefinery design. For instance, GHG emissions, safety and also social aspects as job generation potential. However this criteria was not included in this paper because the main goal of the optimization problem was to select the most promising process pathway using very few data.

Moreover, other important result of the optimization model are both the raw materials and intermediates distribution (previously defined in the methodology section). In this case it is evident that sugarcane bagasse must be spitted in two streams. A fraction going to be treated in the hemicellulose hydrolysis stage (69.4 %) and the other going directly to energy cogeneration (30.6 %). On the other hand, it is also important the result of glucose that is spitted into two streams for PHB production (33.42 %) and ethanol production (66.58 %) (see figure 2). This result was used to simulate the entire process pathway in Aspen Plus, which is also compared to cogeneration and an arbitrary distribution. Table 5. Optimization model solution. Variable

Value

Units

mbagasse

68.0364

Tonne/h

mbagasse,1−8

47.2152

Tonne/h

mbagasse,9

20.8212

Tonne/h

X2 X3

1 0

-

mCellulose , mCellulose,2

17.7765

Tonne/h

mCellulose,3

0

Tonne/h

mglu cos e , mglu cos e,2

15.8211

Tonne/h

mglu cos e,7

5.3871

Tonne/h

X5 X6

0 1

-

mglu cos e,5−6, mglu cos e,6

10.4340

Tonne/h

mglu cos e,5

0

Tonne/h

mEthanol ,4

5.1127

Tonne/h


22

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mEthanol ,6

4.9373

Tonne/h

m Ethanol

10.05

Tonne/h

mElectricity

198000

MJ/h

m PHB

1.67

Tonne/h

Z

22448.1438

USD/h

Besides, the effects of varying both PHB price (reduction of 50 %) and bagasse flowrate drive to interesting results. These results are presented in figure 3. As can be seen these two parameters have significant effects on economic margin and productivity targets. The necessary bagasse flowrate to cover the lower bounds of eqs. 26 to 28 correspond to 43.65 tonne/h for both standard PHB price and reduced PHB price (50%). As presented in figure 3a, the economic margin decreases when the bagasse flow does as well. This is reflected in the first 3 points of the curves. On the other hand, the effect of PHB price reduction over economic margin is notable. For the solution shown in table 5 a reduction of approximately 11.61% in the economic margin of the entire biorefinery is obtained when the PHB price is 50% decreased. The latter is significantly interesting because it demonstrates that the biopolymer can be commercialized cheaper and the biorefinery is still having a good economic performance (grey curve figure 3a). This is particular relevant because some biopolymers can be introduced in the Colombian context and become an interesting option to cover the demands in a competitive way. In terms of productivity capacities, the sensitive analysis revealed that the feedstock distribution (percentages shown in figure 2) can vary and also the reduction of PHB price can drive to different production targets. The latter also affects the economic performance. However the productivity is in the bounds shown in eqs. 26 to 28 for each product.


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Figure 3: Sensitive analysis of the optimization problem. Effect of PHB selling price reduction (50%) and cane bagasse flowrate (Availability).

3.2. Process Simulation. Three processes (scenarios) were evaluated in order to compare the techno-economic and environmental viability of each one. The first process consists on energy cogeneration from bagasse, which is its current use. The second process consists on an arbitrary distribution of raw materials and intermediates. This distribution consists on 100% of cane bagasse to be treated in hemicellulose hydrolysis. The cellulose hydrolysis was performed using the dilute acid technology. The ethanol production was done by the fermentation of the xylose-glucose mixture


24

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using recombinant Z. mobilis. On the other hand, the 70% of glucose was used for ethanol production and the remaining 30% for PHB production. The third process consists on the optimized pathway resulting from the optimization problem described above (see table 5). The difference in this scenario compared with the one shown in the optimization section is the inclusion of further assessment, for instance, the inclusion of all raw materials needed (e.g. water for dilution, dilute acid, lime), a deep technical assessment which includes pretreatments and downstream processing, waste treatment to reduce the potential environmental impact. Also energy and mass integration were considered to achieve a better performance of each configuration. For instance, each configuration was heat integrated using pinch analysis. This served to determine the cooling and heating targets. In this way the heat produced in the cogeneration schemes was able to cover the heating targets after the integration. The cooling target was covered by external supply for all the processes. On the other hand, the mass integration was used mainly for water sources using the targeting methodology presented by ElHalwagi, 2012

65

. This approach helps to decrease the overall water supply in the biorefinery,

which also helps in an important decrease in wastewater volume. At this point it is very important to highlight that the benefit of using the optimization model is not only a possible screening of technologies, but also the selection of the most promising materials distribution. Then the optimization problem served as important criteria in the pre-selection of technologies and distributions in a biorefinery. This is done because of the complexity that a biorefinery represents when a multiproduct portfolio is considered in the same facility. Nevertheless, its results must be evaluated, corroborated and compared with other processes. In this way, this is the main reason why the optimized pathway should be analyzed in a deeper way using the just


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described criteria. Table 6 summarizes the production capacity and yields for ethanol, PHB and electricity according to the simulation procedure described in the methodology section.

Table 6. Production capacities and yields for each product that conforms the biorefinery. Biorefinery Product Fuel ethanol

PHB

Electricity

Scenario Cogeneration Arbitrary Optimized Cogeneration Arbitrary Optimized Cogeneration Arbitrary Optimized

Production Unit Value L/day 0.00 L/day 438041.00 L/day 302881.00 Tonne/day 0.00 Tonne/day 51.65 Tonne/day 39.92 MW 123.28 MW 50.08 MW 75.77

Yield Unit L. Ethanol/Tonne bagasse L. Ethanol/Tonne bagasse L. Ethanol/Tonne bagasse kg PHB/Tonne bagasse kg PHB/ Tonne bagasse kg PHB/ Tonne bagasse MJ/ Tonne bagasse MJ/ Tonne bagasse MJ/ Tonne bagasse

Value 0 264.45 182.85 0.00 31.18 24.10 6430.44 2612.35 3952.33

Product composition Unit Value % (wt) Ethanol 99.9 % (wt) Ethanol 99.8 % (wt) PHB 99.7 % (wt) PHB 99.8 -

Bagasse flowrate: 68.0364 tonne/h for all scenarios As shown in Table 6 when no optimization is done, productivity is higher for ethanol and PHB, but it is lower for electricity. For the optimized scenario, productivity is very close to that one calculated in the optimization problem. For instance, the upper limit of fuel ethanol in the problem solution was 300000 liters per day (10.05 tonne/h) and for the optimized configuration 302881 liters per day, which is very close. Also for PHB is slightly lower but also very close. Electricity yield is different and far because in the simulation procedure biomass generated by Cuapriavidus necator in PHB fermentation was also included for cogeneration and yield significantly increased. It is very important to note that this biomass was not included in the optimization model because the data referred to its efficiency was not able. As explained previously the model is fed with very few data and this biomass flowrate is not easy to get before the complete modeling of the PHB extraction. However the solution of the optimization model gives an important idea on which is the most promising process pathway. This biomass was used in cogeneration system as a surplus to produce more electricity and heat than the predicted in the


26

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optimization model. This is considered an alternative to account in waste valorization. The usage of cell biomass coupled with cogeneration systems was previously proposed by Moncada et al., 2012 2.

3.3. Economic Evaluation. One of the best alternatives for reducing production costs is to decrease the energy consumption during the production process by implementing more energy-efficient and better performing technologies. The energy consumption results from the simulations were used to assess the implication of the biorefinery distribution choices and, the technologies according to energy consumption, and the effect of energy savings on production costs. Energy consumption is calculated for the entire biorefinery configuration. The global energy consumption corresponds to 1890.04, 37800.78, 24255.55 MJ per tonne of bagasse for cogeneration, arbitrary distribution and optimized distribution respectively. For all the arbitrary and optimized distributions the process that represents the higher energy consumption is fuel ethanol production. This could be explained because this is one of the processes of the biorefinery that has more processing units and materials conditioning operations. On the other hand, an important issue to take into account in the cogeneration systems is that steam generation is fully integrated in order to save energy consumption and take advantage of self-process energy. Therefore, energy consumption in cogeneration system is relatively low compared with the total consumption in the fuel ethanol process. PHB process summarizes energy consumption especially in recovery process (extraction and spray drying). One of the goals of the economic assessment is to determine the total production cost and the income by product sales. In this way the total production cost of electricity is calculated per MJ


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generated for the Colombian base case (energy cogeneration from sugarcane bagasse), and ethanol and PHB production cost were calculated for the other schemes. Is important to consider that energy generated by biomass fired systems for the arbitrary and the optimized scenarios were considered as a saving in utilities supply in total production cost for both PHB and fuel ethanol. Indeed Tables 7 to 9 show the production cost of electricity, fuel ethanol and PHB respectively.

Given this, distribution and technologies directly affect yields and production costs. The operating cost includes various aspects inherent to the production process such as raw materials, utilities, labor and maintenance, general plant costs and general administrative costs. Annualized capital costs are also included. The comprehensive evaluation of each aspect leads to the plants operating costs. This evaluation was based on the results obtained from simulation and the Aspen Economic Evaluator package, adapted to Colombian parameters. For most industrial processes the raw material represents approximately more than 50% of total production cost 2. To estimate the raw material costs in the biorefinery process, it was considered the distributions described previously. Nevertheless this value for all products (tables 7-9) is lower basically due to secondgeneration feedstock that only has the transportation charges associated. Another important aspect to take into account in a biorefinery is the job generation potential. In the labor cost sections (table 7-9) the cost associated is related to the workers that currently labor in the processing section. The minimum number of operators per shift is 5, 8 and 8 for cogeneration, arbitrary and optimized pathway respectively. This also represents an important social aspect because a biorefinery that includes more products will need more people that supervise the process independently of the feedstock processed (flowrate). On the other hand, the general and


28

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administrative cost counts for administrative employees indirectly (tables 7 to 9), which also represent one of the higher fraction (share) of total production cost. Thus employees generated in the start-up period of the process are considered in the depreciation of capital, for instance the related jobs in the civil works, pipelines, isolation, electric works, and instrumentation among many others. On the other hand, in table 7 can be seen that the utilities section has no cost associated. This is due to the usage of fresh water in the heat steam recovery system, which is charge in the raw materials section. In tables 8 and 9 it can be seen that utilities is the cost that represent the highest portion of total productions inclusive after the energy integration. However, it can be seen in table 8 that ethanol production cost was very similar for the arbitrary and optimized scenarios. In the case of PHB it can be seen that production cost in the optimized scenario is lower than the arbitrary distribution. This is due to the increment in the production capacity (see table 6) for PHB. Table 7. Total production cost of one MJ by sugarcane bagasse cogeneration Item Raw Materials Operating Labor Utilities Operating Charges, Plant Overhead, Maintenance General and Administrative Cost Depreciation of Capital Total

Cost and Share (%) Cost Share Cost Share Cost Share Cost Share Cost Share Cost Share Cost Share

Electricity USD/MJ 0.0076 33.08 0.0000 0.13 0.0000 0.00 0.0006 2.45 0.0121 52.98 0.0026 11.37 0.0229 100.00

As can be seen, for all the scenarios PHB production cost is higher than sale price. The most impact on total production cost is utilities, especially in the downstream processing which


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generally requires a large amount of energy. Nevertheless, a lower production cost of fuel ethanol and the important contribution of electricity on sales can subsidy completely the other products and also feasibility can be achieved. Figure 3 shows the production cost to sale price ratio for each product in order to make clarity on profitability of each product. On the other hand, Figure 4 shows the share on sales of each product for each scenario.

Table 8. Total production cost of one ethanol liter. Cost and Share (%)

Item Raw Materials Operating Labor Utilities Operating Charges, Plant Overhead, Maintenance General and Administrative Cost Depreciation of Capital Total

Cost Share Cost Share Cost Share Cost Share Cost Share Cost Share Cost Share

Ethanol USD/L Arbitrary Optimized 0.1322 0.1931 23.75 34.01 0.0005 0.0008 0.09 0.13 0.2834 0.1351 50.92 23.78 0.0078 0.0122 1.40 2.15 0.1015 0.1781 18.24 31.36 0.0312 0.0486 5.60 8.56 0.5565 0.5679 100.00 100.00

Table 9. Total production cost of one PHB kilogram. Cost and Share (%)

Item Raw Materials Operating Labor Utilities Operating Charges, Plant Overhead, Maintenance General and Administrative Cost Depreciation of Capital Total

Cost Share Cost Share Cost Share Cost Share Cost Share Cost Share Cost Share

PHB USD/kg Arbitrary Optimized 0.3611 0.4837 9.12 14.53 0.0045 0.0058 0.11 0.17 2.4044 1.0251 60.69 30.80 0.0661 0.0928 1.67 2.79 0.8613 1.3518 21.74 40.62 0.2644 0.3690 6.67 11.09 3.9618 3.3281 100.00 100.00


30

1.21 1.20 1.00 0.80 0.60 0.40 0.20 0.00

0.95

0.94

0.90

0.85

0.80

0.79

0.75

0.70

Electricity Cogeneration

Sale price/Total production cost

1.40


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



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2.24 2.23

2.23

2.22 2.21 2.20 2.19

2.18

2.18 2.17 2.16

PHB Arbitrary

Optimized

Ethanol Arbitrary

Optimized

Figure 4: Sale price-total production cost ratio for each product in the different scenarios 6.

As can be seen in Figure 4, when the optimized scheme was considered, the ratio of sale pricetotal production cost is lower for fuel ethanol. This depends on the energy cost and the total production volume. In the case of PHB the optimized scenario shows a higher sale price-total production cost ratio. The latter is due to the increment in production capacity for PHB. For the cogeneration alternative, it can be seen that the sale price/total production cost is greater than the unity. However it is not high enough to guarantee a high economic income. In this way, the biorefinery configurations that consider integrated electricity production will receive benefits in terms of heating requirements, which leads to an important decreased in total production cost in the entire biorefinery. In the same way, the electricity surplus will also generate an extra income that enhances the global biorefinery performance in economic terms. Given this, another criteria to calculate the feasibility of the entire biorefinery was included. This consists on the inclusion of the total production cost of all the products related to the sales of all of them. Thus, an economic margin can be calculated as the relation of sales and total production costs. In this way, Figure 6


31


shows the economic margin for the entire biorefinery for each scenario. As can be seen, the scenario with the highest economic margin is the optimized pathway, because the distribution decision and technology selection were the most promising. Then the optimized pathway actually predicted the most promising pathway in economic terms. This is an interesting result because a simple distribution and technology screening served as criteria to enhance biorefinery incomes by its distributions. At this point, it is very important to mention that a biorefinery should not be seen in separated products. It should be seen as a multiproduct portfolio where all the products contribute in incomes and also share costs 2. It was also demonstrated that products that could also be unfeasible to be produced in a separated way can be feasible if an integrated biorefinery is considered 2.

120 100 80

Share %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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60 40 20 0

Ethanol

Electricity

Cogeneration

Arbitrary

PHB Optimized

Figure 5: Share on sales of each product for the different scenarios on a sugarcane bagasse based biorefinery.


32

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60 53.83

Economic Margin %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


50

44.15

40 30 17.48

20 10 0

Economic Margin

Cogeneration

Arbitrary

Optimized

Figure 6: Economic margin for all scenarios in a sugarcane bagasse based biorefinery.

On the other hand, one of the most important aspects to consider in a biorefinery design is the potential environmental impact (PEI) combined with the economic and social point of views. Nevertheless, because an integration of waste materials is considered, for example C. Necator for cogeneration and stillage concentration for fertilizer production, a lower potential environmental impact is expected. However, a further analysis must be considered. In this way, the environmental assessment is included in the following section.

3.4. Environmental Assessment The environmental assessment is based on the criteria of the impacts named in the methodology item. The three scenarios were evaluated. The results of the potential environmental impact per kilogram of products are presented in Figure 7. The results show that the friendliest configuration is the one that only considers cogeneration followed by the optimized pathway and the arbitrary distribution respectively. For the scenario that only includes


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cogeneration, the impacts that most contribute to the potential environmental impact are the acidification and photochemical oxidation potentials. This because the main emissions are gases and low environmental impact is obtained. However, it will be expected that the global warming potential (GWP) will be also higher. Nevertheless, the weighted sum of all impacts leads to a global result. This means that the GWP does not contribute in a strong way to the total impact and other categories contribute with higher individual impacts. On the other hand, for the scenarios that include acid hydrolysis in its technical sequence (arbitrary and optimized), different impact categories show higher values (see figure 7). This is due to the intermediate or undesirable products formed in the different reaction steps as the case of furfural and hydroxymethylfurfural. These intermediates were neutralized in overliming processes. However, a very low concentration in the precipitated solution (mainly gypsum) significantly affects the potential environmental impact. For these reasons the environmental impact is higher in these processing routes, and the human toxicity by ingestion and terrestrial toxicity potential are the highest impacts. These categories lead to a higher environmental impact than the base case (cogeneration). Another significant aspect to include in the biorefinery analysis is the equivalent carbon dioxide as a measure of GHG emissions. The GHG emissions represented as equivalent carbon dioxide in kilograms per kilogram of processed cane bagasse are shown in figure 8. For all the three scenarios it is very important to note that the GHG calculations only include the emissions from the mass balance of the cogeneration unit and the fermentations stages producing fuel ethanol and PHB. Basically these emissions are considered as biological emissions and they do not count for atmospheric emissions. The emissions due to energy consumption are not considered because the steam generated in the cogeneration unit covers the energy requirements of the biorefinery.


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As can be seen, the cogeneration process presents a higher value of carbon dioxide equivalent per kilogram of cane bagasse because the exhausted gases leaving the process are higher than those released in the arbitrary distribution and the optimized pathway. On the other hand, the arbitrary distribution shows a higher value in emissions because the amount of CO2 produced in the fermentation stage is higher than the one of the optimized pathway. However, this results in a balancing between cogeneration and fermentations because more electricity can be produced when cane bagasse is used as fuel, instead of lignin and cellular biomass as the case of the arbitrary scenario. Consequently a lower energy value leads to higher biological emissions.

Figure 7: Potential Environmental impact for the different scenarios.


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Figure 8: Biological CO2-eq produced in the different scenarios.

CONCLUSIONS The formulation of optimization problems in biorefinery design plays an important role, especially for decision support in distribution within the selection of the most promising process pathway in terms of technology. This study is an important case where optimization is a start point in the design of a sugarcane bagasse based biorefinery. This is highlighted because with very few data a promising pathway and material distribution was obtained using an optimization formulation. The latter served as important criteria in the pre-selection of technologies and distributions in a biorefinery, which was used to feed a robust design using the knowledge-based approach. In order to evaluate the prediction done in the optimization works, a comparison of


36

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three different process configurations for a sugarcane bagasse was considered. The first one consisted in energy cogeneration from cane bagasse, the second one an arbitrary distribution and technology and the third one the optimized pathway distributions and technologies. In the last case, the optimized pathway leads to a lower energy consumption than the arbitrary distribution but higher than cogeneration system. However, the optimized scenario presented a higher economic margin, a lower potential environmental impact than the arbitrary one and lower GHG biological emissions. It is very important to note that the optimization platform was considered as a start point in the decision making for distributions and technologies. In this way, the overall result of the analyzed scenarios showed that all of them were feasible. Nevertheless, given the comprehensive analysis developed, the recommended configuration for a sugarcane bagasse biorefinery in Colombia is the optimized scenario

Acknowledgments The authors express their acknowledgments to the National University of Colombia at Manizales, Amazonas and Orinoquia for financial support.

Supporting Information. Table 1. Kinetic Models summary. Technology

Cellulose Hydrolysis. Dilute acid

Kinetic Model

Parameters

Cellulose to Glucose

ko ,1 = 6.7 ⋅ 1016 min

 E  r1 = ko ,1 ⋅ Cacid n1 ⋅ exp  − a ,1   R ⋅T 

ko ,2 = 3.7 ⋅ 1016 min

Glucose to Hydroxymethylfurfural

Ea ,1 = 129800 J ⋅ mol −1 Ea ,1 = 98400 J ⋅ mol −1


n = 1.5

Cacid= Acid concentration in weight percentage

n2 = 0.5

1

Configuration/ Conditions Reactor Type: CSTR Temperature: 373.15 K Acid concentration: 4 % w/w


37

Ref.

33


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Page 38 of 44

Cellulose to Cellobiose: r1 =

k1r CE1B RsCs CG2 C C 1+ + G + Xy K1IG 2 K1IG K1IXy

k1r = 22.3 g ⋅ mg −1 ⋅ h −1 k2 r = 7.18 g ⋅ mg −1 ⋅ h −1

Cellulose to glucose r2 =

(

k3 r = 285.5 g ⋅ mg −1 ⋅ h −1

)

k2 r CE1 B + CE2 B Rs Cs CG2 C C 1+ + G + Xy K 2 IG 2 K 2 IG K 2 IXy

Ea1 = Ea 2 = Ea 3 = −23190 J ⋅ mol −1

K1IG 2 = 0.015 g L K1IG = 0.1 g L

Cellobiose to Glucose r3 =

Cellulose Hydrolysis. Enzymatic

K1IXy = 0.1 g L

k3r CE2 F CG2  C C K 3M  1 + G + Xy  K K 3 IG 3 IXy 

K 2 IG = 0.04 g L

Ei max K iad CEiF Cs 1 + K iad CEiF

K 3 IXy = 201 g L

K 3 M = 24.3 g L

CEiT = C EiF + C EiB

E1max = 0.06 g g

Substrate Reactivity

E2max = 0.01 g g

RS = CS S 0

Ea ,1 = 111600 J ⋅ mol −1

Temperature dependence  −E  kir = kir (T1 ) exp  ai   RT  CG2 [ Cellobiose ] CG [ Glucose ] C Xy [ Xylose] CS [Cellulose]

K1ad = 0.4 g g

Hemicellulose to Xylose

ko ,1 = 1.4 ⋅ 1014 min


ko ,2 = 3.3 ⋅ 1010 min

Xylose to Furfural

K 2 ad = 0.1 g g

Ea ,1 = 95700 J ⋅ mol


Reactor Type: CSTR Temperature: 373.15 K

−1

n = 0.68

Combined Cycle Gasification based on stoichiometric approach using free Gibbs energy minimization method.

n2 = 0.4

Chemical approach

Involved components. CH 4 , CO, CO2 , H 2O, NOx , SOx , N 2O, O2 , N 2 , Biomass

33

Acid concentration: 4 % w/w Reactor Type: Gasified

1

Cacid= Acid concentration in weigth percentage

Biomass gasification

Temperature: 328.15 K

K 3 IG = 3.9 g L

Enzyme

Dilute acid pretreatment. Hemicellulose Hydrolysis

34

K 2 IXy = 0.2 g L

Enzyme adsorption CEiB =

Reactor Type: CSTR

K 2 IG 2 = 132.0 g L

  + CG2 

Equilibrium

Fluidized bed Pressure: 60 bar

35

Biomass Growth  S r  S r  rx = µm  1 −  C x  S r + K sr  S m 

µ m = 0.78 h −1

Carbon source uptake

S m = 0.3

rsc = −k4 rx − k5Cx

n = 0.24

Nitrogen source uptake

k1 = 0.2604

rc = − k3rx

k2 = 0.0301

PHB production

k3 = 0.6511

n

PHB production. Glucose Fermentation

K sr = 0.29

rp = k1rx + k2Cx

k4 = 4.503

C Sr = sn Csc

Fermentor Type: Continuous Temperature: 30°C

k5 = 0.0001

Csn [ Nitrogen source ] Csc [ Carbon source]


38

36

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µ max = 0.426 h −1 K i = 0.002

Cell growth rx =

Ethanol Production. Saccharomyces cerevisiae

m

n

 X   P  exp ( − K i S ) 1 −  1 −  X Ks + S  X max   Pmax 

µmax S

X max = 54.474

Ethanol Production

Yx = 9.763

rp = Ypx rx + m p X

Yp x = 0.03831

Substrate consumption

K s = 4.1

r rs = x + mx X Yx

m p = 0.1

X [biomass ]

38

Temperature: 37°C Microorganism: Saccharomyces Cerevisiae

mx = 0.2

S [ susbtrate ]

m =1 n = 1.5

Glucose consumption    Cs1 Cp − Pis1   Kis1  Rs1 = α ⋅ qsmax1   ⋅ 1 − ⋅  ⋅ Cx  Kss1 + Cs1   Pms1 − Pis1   Kis1 + Cs1 

Xylose consumption    Cs2 Cp − Pis2   Kis2  Rs2 = (1 − α ) qsmax 2   ⋅ 1 − ⋅  ⋅ Cx Kss Cs Pms + 2 2   2 − Pis2   Kis2 + Cs2  

Ethanol Production, Recombinant Zymomonas Mobilis

Pmax = 86.072

Ethanol Production      Cs1 Cp − Pip1   Kip1   α ⋅ qpmax1    ⋅ 1 − ⋅ + + − + Ksp Cs Pmp Pip Kip Cs  1 1   1 1  1 1   Rp =   ⋅ Cx    Cs2 Cp − Pip2   Kip2     (1 − α ) ⋅ qpmax 2  Ksp + Cs  ⋅ 1 − Pmp − Pip  ⋅  Kip + Cs    2 2   2 2   2 2  

Fermentor Type: Continuous Stired Tank Bioreactor

α = 0.65 µmax1 = 0.31 µ max 2 = 0.1 Ksx1 = 1.45 Ksx2 = 4.91 Pmx1 = 57.2 Pmx2 = 56.3 Kix1 = 200

Kix2 = 600

Pix1 = 28.9 Pix2 = 26.6 qsmax1 = 10.9 qsmax 2 = 3.27

Fermentor Type: Continuous Stired Tank Bioreactor

Kss1 = 6.32 Kss2 = 0.03 Pms1 = 75.4 Pms2 = 81.2 Kis1 = 186

Kis2 = 600

Pis1 = 42.6

Pis2 = 53.1

Biomass growth

qpmax1 = 5.12 qpmax 2 = 1.59

     Cs1 Cp − Pix1   Kix1   α ⋅ µmax1    ⋅ 1 − ⋅ +  Ksx1 + Cs1   Pmx1 − Pix1   Kix1 + Cs1    Rx =   ⋅ Cx       Cs Cp − Pix Kix 2 2 2  (1 − α ) ⋅ µ  ⋅ 1 − ⋅   max 2    Ksx2 + Cs2   Pmx2 − Pix2   Kix2 + Cs2   

Ksp1 = 6.32

Ksp2 = 0.03

Pmp1 = 75.4 Pmp2 = 81.2 Kip1 = 186

Kip2 = 600

Pip1 = 42.6

Pip2 = 53.1

37

Temperature: 35°C Microorganism: Zymomonas Mobilis

Table 2. Nomenclature of the optimization problem. Variable

mbagasse,9

Description Mass of products (PHB, Ethanol) tonne/h Sale price of ethanol and PHB in USD/kg Generated power by biomass gasification in MJ/h Electricity price in USD/MJ Bagasse flowrate in tonne/h Bagasse price in USD/tonne Total sugarcane bagasse flowrate in tonne/h Sugarcane bagasse flowrate that is sent to treatment though technologies 1 to 8 in tonne/h Sugarcane bagasse flowrate that is sent directly to gasification in tonne/h

mCellulose mHemicellulose

Cellulose flowrate present in treatment through technologies 1 to 8 in tonne/h Hemicellulose flowrate present in treatment through technologies 1 to 8 in tonne/h

mi pi

wk pk mbagasse pbagasse

mbagasse mbagasse,1−8


39


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

mLignin

Lignin flowrate present in treatment through technologies 1 to 8 in tonne/h

yi Xi

Yield corresponding to each technological option (see table 2) Binary variables for each of the technologies when disjunction is necessary

mxylose,1

Xylose flowrate resulting from technology 1 in tonne/h

mCellulose,2

Cellulose flowrate to be treated in technology 2 in tonne/h

mCellulose,3

Cellulose flowrate to be treated in technology 3 in tonne/h

wElectricity

Generated work by technologies 8 and 9 in MJ/h

mglucose

Glucose flowrate to be transform in technologies 5,6 and 7 in tonne/h

mglucose,5−6

Glucose flowrate to be transform in technologies 5 and 6 in tonne/h

mglucose,7

Glucose flowrate to be transform in technology 7 in tonne/h

mglucose,5


mglucose,6


mPHB

PHB flowrate obtained in technology 7 in tonne/h

mEthanol ,4

Ethanol flowrate obtained in technology 4 in tonne/h

mEthanol ,5


mEthanol ,6


mEthanol

Ethanol flowrate resulting from technologies 4,5 and 6 in tonne/h

Page 40 of 44

REFERENCES 1. Asocaña, Informe Anual 2010-2011. Sector Azucarero Colombiano. Asociación de cultivadores de caña de azúcar de Colombia 2011, 1, 1-102. 2. Moncada, J.; El-Halwagi, M. M.; Cardona, C. A., Techno-economic analysis for a sugarcane biorefinery: Colombian case. Bioresource Technology 2012, http://dx.doi.org/10.1016/j.biortech.2012.08.137. 3. Cherubini, F., The biorefinery concept: Using biomass instead of oil for producing energy and chemicals. Energy Conversion and Management 2010, 51, (7), 1412-1421. 4. Cherubini, F.; Ulgiati, S., Crop residues as raw materials for biorefinery systems - A LCA case study. Applied Energy 2010, 87, (1), 47-57. 5. Clark, J. H., Green chemistry for the second generation biorefinery—sustainable chemical manufacturing based on biomass. Journal of Chemical Technology & Biotechnology 2007, 82, (7), 603-609. 6. Posada, J. A.; Rincón, L. E.; Cardona, C. A., Design and analysis of biorefineries based on raw glycerol: Addressing the glycerol problem. Bioresource technology 2012, 111, (0), 282293. 7. El-Halwagi, M. M., Chapter 21 - Design of Integrated Biorefineries. In Sustainable Design Through Process Integration, Butterworth-Heinemann: Oxford, 2012; pp 365-373.


40

Page 41 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


8. Dimian, A. C., Renewable raw materials: chance and challenge for computer-aided process engineering. In Computer Aided Chemical Engineering, Valentin, P.; Paul Serban, A., Eds. Elsevier: 2007; Vol. Volume 24, pp 309-318. 9. Shabbir, Z.; Tay, D. H. S.; Ng, D. K. S., A hybrid optimisation model for the synthesis of sustainable gasification-based integrated biorefinery. Chemical Engineering Research and Design 2012, 90, (10), 1568-1581. 10. Bao, B.; Ng, D. K. S.; Tay, D. H. S.; Jiménez-Gutiérrez, A.; El-Halwagi, M. M., A shortcut method for the preliminary synthesis of process-technology pathways: An optimization approach and application for the conceptual design of integrated biorefineries. Computers & Chemical Engineering 2011, 35, (8), 1374-1383. 11. Kokossis, A. C.; Yang, A., Future System Challenges in the Design of Renewable Bioenergy Systems and the Synthesis of Sustainable Biorefineries. In Design for Energy and the Environment, CRC Press: 2009; pp 107-123. 12. Kokossis, A. C.; Yang, A., On the use of systems technologies and a systematic approach for the synthesis and the design of future biorefineries. Computers & Chemical Engineering 2010, 34, (9), 1397-1405. 13. Klatt, K. U.; Marquardt, W., Perspectives for process systems engineering--Personal views from academia and industry. Computers & Chemical Engineering 2009, 33, (3), 536-550. 14. Zondervan, E.; Nawaz, M.; de Haan, A. B.; Woodley, J. M.; Gani, R., Optimal design of a multi-product biorefinery system. Computers & Chemical Engineering 2011, 35, (9), 1752-1766. 15. Ng, D. K. S.; Pham, V.; El-Halwagi, M. M.; Jiménez-Gutiérrez, A.; Spriggs, D. In A hierarchical approach to the synthesis and analysis of integrated biorefineries, Proceeding of Seventh International Conference on Foundations of Computer-Aided Process Design, 2009; 2009; pp 425-432. 16. Pham, V.; El Halwagi, M., Process synthesis and optimization of biorefinery configurations. AIChE Journal 2011, 58, (4), 1212-1221. 17. Kasivisvanathan, H.; Ng, R. T. L.; Tay, D. H. S.; Ng, D. K. S., Fuzzy optimisation for retrofitting a palm oil mill into a sustainable palm oil-based integrated biorefinery. Chemical Engineering Journal 2012, 200–202, (0), 694-709. 18. Tay, D. H. S.; Ng, D. K. S.; Sammons Jr, N. E.; Eden, M. R., Fuzzy optimization approach for the synthesis of a sustainable integrated biorefinery. Industrial & Engineering Chemistry Research 2011, 50, (3), 1652-1665. 19. Ponce-Ortega, J. M.; Pham, V.; El-Halwagi, M. M.; El-Baz, A. A., A disjunctive programming formulation for the optimal design of biorefinery configurations. Industrial & Engineering Chemistry Research 2012, 51, (8), 3381-3400. 20. Ojeda, K. A.; Sánchez, E. L.; Suarez, J.; Avila, O.; Quintero, V.; El-Halwagi, M.; Kafarov, V., Application of computer-aided process engineering and exergy analysis to evaluate different routes of biofuels production from lignocellulosic biomass. Industrial & Engineering Chemistry Research 2010, 50, (5), 2768-2772. 21. Martín, M.; Grossmann, I. E., Superstructure optimization of Lignocellulosic Bioethanol plants. Computer Aided Chemical Engineering 2010, 28, 943-948. 22. Kokossis, A. C.; Yang, A.; Tsakalova, M.; Lin, T. C., A systems platform for the optimal synthesis of biomass based manufacturing systems. Computer Aided Chemical Engineering 2010, 28, 1105-1110.


41


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 44

23. Tan, R. R.; Ballacillo, J. A. B.; Aviso, K. B.; Culaba, A. B., A fuzzy multiple-objective approach to the optimization of bioenergy system footprints. Chemical Engineering Research and Design 2009, 87, (9), 1162-1170. 24. Sammons, N.; Yuan, W.; Eden, M.; Aksoy, B.; Cullinan, H., Optimal biorefinery product allocation by combining process and economic modeling. Chemical Engineering Research and Design 2008, 86, (7), 800-808. 25. Sánchez, Ó. J.; Cardona, C. A., Conceptual design of cost-effective and environmentallyfriendly configurations for fuel ethanol production from sugarcane by knowledge-based process synthesis. Bioresource technology 2012, 104, 305-314. 26. Cardona, C. A.; Sánchez, Ó. J., Fuel ethanol production: Process design trends and integration opportunities. Bioresource technology 2007, 98, (12), 2415-2457. 27. Cardona, C.; Quintero, J.; Paz, I., Production of bioethanol from sugarcane bagasse: status and perspectives. Bioresource technology 2010, 101, (13), 4754-4766. 28. Pandey, A.; Soccol, C. R.; Nigam, P.; Soccol, V. T., Biotechnological potential of agroindustrial residues. I: sugarcane bagasse. Bioresource technology 2000, 74, (1), 69-80. 29. Quintero, J. A.; Rincón, L. E.; Cardona, C. A., Chapter 11 - Production of Bioethanol from Agroindustrial Residues as Feedstocks. In Biofuels, Ashok, P.; Christian, L.; Steven, C. R.; Claude-Gilles, D.; Edgard GnansounouA2 - Ashok Pandey, C. L. S. C. R. C.-G. D.; Edgard, G., Eds. Academic Press: Amsterdam, 2011; pp 251-285. 30. Sánchez, Ó. J.; Cardona, C. A., Trends in biotechnological production of fuel ethanol from different feedstocks. Bioresource technology 2008, 99, (13), 5270-5295. 31. Vaz Rossell, C. E.; Mantelatto, P. E.; Agnelli, J. A. M.; Nascimento, J., Sugar-based Biorefinery–Technology for Integrated Production of Poly (3-hydroxybutyrate), Sugar, and Ethanol. In Biorefineries Industrial Processes and Products, Kamm, B.; Gruber, P. R.; Kamm, M., Eds. 2006; pp 209-226. 32. Posada, J. A.; Naranjo, J. M.; López, J. A.; Higuita, J. C.; Cardona, C. A., Design and analysis of poly-3-hydroxybutyrate production processes from crude glycerol. Process Biochemistry 2011, 46, (1), 310-317. 33. Jin, Q.; Zhang, H.; Yan, L.; Qu, L.; Huang, H., Kinetic characterization for hemicellulose hydrolysis of corn stover in a dilute acid cycle spray flow-through reactor at moderate conditions. Biomass and Bioenergy 2011. 34. Morales-Rodriguez, R.; Gernaey, K. V.; Meyer, A. S.; Sin, G., A Mathematical Model for Simultaneous Saccharification and Co-fermentation (SSCF) of C6 and C5 Sugars. Chinese Journal of Chemical Engineering 2011, 19, (2), 185-191. 35. Najjar, Y. S. H., Gas turbine cogeneration systems: a review of some novel cycles. Applied Thermal Engineering 2000, 20, (2), 179-197. 36. Shahhosseini, S., Simulation and optimisation of PHB production in fed-batch culture of Ralstonia eutropha. Process Biochemistry 2004, 39, (8), 963-969. 37. Leksawasdi, N.; Joachimsthal, E. L.; Rogers, P. L., Mathematical modelling of ethanol production from glucose/xylose mixtures by recombinant Zymomonas mobilis. Biotechnology Letters 2001, 23, (13), 1087-1093. 38. Rivera, E. C.; Costa, A. C.; Atala, D. I. P.; Maugeri, F.; Maciel, M. R. W., Evaluation of optimization techniques for parameter estimation: Application to ethanol fermentation considering the effect of temperature. Process Biochemistry 2006, 41, (7), 1682-1687. 39. Larrahondo, J. E., Calidad Caña de Azucar. Cenicaña. El cultivo de la caña en la zona azucarera de Colombia 1995, 337-354.


42

Page 43 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


40. FedeBiocombustibles, Ethanol anhidro de caña. Federación Nacional de Biocombustibles de Colombia. Cifras Informativas del Sector Biocombustible 2012, January 2012, 1-6. 41. Castillo, E. F., Cogeneración en el sector azucarero colombiano. Acolgen. Asociación Colombiana de Generadores de Energía Eléctrica. 2009, Segundas Jornadas de Generación. 42. Quintero, J.; Montoya, M.; Sánchez, O.; Giraldo, O.; Cardona, C., Fuel ethanol production from sugarcane and corn: comparative analysis for a Colombian case. Energy 2008, 33, (3), 385-399. 43. Lee, S.; Grossmann, I. E., New algorithms for nonlinear generalized disjunctive programming. Computers and Chemical Engineering 2000, 24, (9), 2125-2142. 44. Balas, E., Disjunctive programming. 50 Years of Integer Programming 1958-2008 2010, 283-340. 45. Türkay, M.; Grossmann, I. E., Disjunctive programming techniques for the optimization of process systems with discontinuous investment costs-multiple size regions. Industrial & Engineering Chemistry Research 1996, 35, (8), 2611-2623. 46. Schmidt, C. W.; Grossmann, I. E., Optimization models for the scheduling of testing tasks in new product development. Industrial & Engineering Chemistry Research 1996, 35, (10), 3498-3510. 47. Lee, S.; Grossmann, I. E., Global optimization of nonlinear generalized disjunctive programming with bilinear equality constraints: applications to process networks. Computers & Chemical Engineering 2003, 27, (11), 1557-1575. 48. Yeomans, H.; Grossmann, I. E., Disjunctive programming models for the optimal design of distillation columns and separation sequences. Industrial & Engineering Chemistry Research 2000, 39, (6), 1637-1648. 49. Rosenthal, R. E., GAMS-A users guide. 2008. Washington, DC, USA: GAMS Development Corporation. 50. Matlab; MathWorks, I.; Natick, M. A., The Language of Technical Computing. 2004. 51. Wooley, R.; Putsche, V. Development of an ASPEN PLUS physical property database for biofuels components; Report NREL/MP-425-20685; National Renewable Energy Laboratory: Golden, CO, USA, 1996; p 38. 52. Cabezas, H.; Bare, J. C.; Mallick, S. K., Pollution prevention with chemical process simulators: the generalized waste reduction (WAR) algorithm—full version. Computers & Chemical Engineering 1999, 23, (4), 623-634. 53. Young, D. M.; Cabezas, H., Designing sustainable processes with simulation: the waste reduction (WAR) algorithm. Computers & Chemical Engineering 1999, 23, (10), 1477-1491. 54. Young, D.; Scharp, R.; Cabezas, H., The waste reduction (WAR) algorithm: environmental impacts, energy consumption, and engineering economics. Waste Management 2000, 20, (8), 605-615. 55. Cardona, C.; Marulanda, V.; Young, D., Analysis of the environmental impact of butylacetate process through the WAR algorithm. Chemical engineering science 2004, 59, (24), 5839-5845. 56. Eggleston, H., 2007 IPCC Guidelines for National Greenhouse Gas Inventories. Forestry 2007, 5, (OVERVIEW), 1-12. 57. Katahira, S.; Ito, M.; Takema, H.; Fujita, Y.; Tanino, T.; Tanaka, T.; Fukuda, H.; Kondo, A., Improvement of ethanol productivity during xylose and glucose co-fermentation by xyloseassimilating S. cerevisiae via expression of glucose transporter Sut1. Enzyme and Microbial Technology 2008, 43, (2), 115-119.


43


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 44 of 44

58. Sedlak, M.; Ho, N. W. Y., Production of ethanol from cellulosic biomass hydrolysates using genetically engineered Saccharomyces yeast capable of cofermenting glucose and xylose. Applied biochemistry and biotechnology 2004, 114, (1), 403-416. 59. Nigam, J., Ethanol production from wheat straw hemicellulose hydrolysate by Pichia stipitis. Journal of biotechnology 2001, 87, (1), 17-27. 60. Preez, J. C.; Bosch, M.; Prior, B., The fermentation of hexose and pentose sugars by Candida shehatae and Pichia stipitis. Applied microbiology and biotechnology 1986, 23, (3), 228233. 61. Govindaswamy, S.; Vane, L. M., Kinetics of growth and ethanol production on different carbon substrates using genetically engineered xylose-fermenting yeast. Bioresource technology 2007, 98, (3), 677-685. 62. Meyrial, V.; Delgenes, J.; Moletta, R.; Navarro, J., Xylitol production from D-xylose byCandida guillermondii: Fermentation behaviour. Biotechnology Letters 1991, 13, (4), 281-286. 63. Mussatto, S. I.; Roberto, I. C., Acid hydrolysis and fermentation of brewer's spent grain to produce xylitol. Journal of the Science of Food and Agriculture 2005, 85, (14), 2453-2460. 64. Silva, C. J. S. M.; Mussatto, S. I.; Roberto, I. C., Study of xylitol production by Candida guilliermondii on a bench bioreactor. Journal of Food Engineering 2006, 75, (1), 115-119. 65. El-Halwagi, M. M., Chapter 3 - Benchmarking Process Performance Through Overall Mass Targeting. In Sustainable Design Through Process Integration, Butterworth-Heinemann: Oxford, 2012; pp 63-88.


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A Colombian Case for Conversion of Sugarcane Bagasse to Ethanol

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