Techno-Economic and Environmental Analysis of Ethanol Production

Article pubs.acs.org/EF

Techno-Economic and Environmental Analysis of Ethanol Production from 10 Agroindustrial Residues in Colombia Sergio H. Duque, Carlos A. Cardona,* and Jonathan Moncada Instituto de Biotecnología y Agroindustria, Departamento de Ingeniería Química, Universidad Nacional de Colombia sede Manizales, Cra. 27 No. 64-60, 170001 Manizales, Colombia S Supporting Information *

ABSTRACT: This study evaluates the feasibility of industrial ethanol production from 10 agroindustrial residues in Colombia by considering production yields, profitability, and environmental impacts. For this analysis, sugar cane bagasse, banana stem, corncob, rice husk, sawdust, woodbark, mango wastes, palm residues, pineapple peel, and plantain peel were considered as feedstocks. For each residue, a techno-economic and environmental assessment was carried out using relevant data generated from process modeling. To do this, diluted acid pretreatment and enzymatic hydrolysis (using cellulases) of the lignocellulosic materials were selected as technologies. Besides, Saccharomyces cerevisiae and Pichia stipitis were selected as fermenting microorganisms under normal conditions. The production obtained from process modeling were compared with experimental ones from other authors. Yields between 0.009 and 0.264 kg of ethanol per kg of residue and production costs about $0.65 per liter of ethanol were obtained. Additionally, an average reduction of 39% on environmental impacts was obtained by the use of raw materials. Finally, it was found that four agroindustrial residues show profit margins and profitabilities of 40% over the cost of commercial raw materials in the process of ethanol production.

1. INTRODUCTION In Colombia, ethanol is mainly produced in sugarmill factories from sugar cane (225 560 ha).1,2 Additionally, it can be produced from alternative crops such as cassava and sugar beet due to increased ethanol demand. However, Colombia achieves a production of 1000 cubic meters of ethanol per day.2 In 2012, an average production of 369.7 million liters2 was reached to cover the 82% of the total demand,1 and a production of 387.8 million liters was projected for 2013. To guarantee profitability, the government regulates ethanol’s price at $0.82 per liter,3 which is approximately 24% above its international price ($0.66 USD/L).4 Nowadays, the gasoline in Colombia has a price of $1.12 dollar per liter,5 and this is used as a mixture 10% anhydrous ethanol.6 Taking into account that ethanol production cost from sugar cane ranged between $0.22 and $0.39 per liter in 2008, it is required to evaluate new alternatives in order to reduce its cost.7,8 Ethanol production from agroindustrial residues has been widely studied in order to find alternatives of biofuels from sources that do not compete with food. The abundance and low cost are the main features of these agroindustrial residues.9 The residues such as sugar cane bagasse, rice husk and coffecut-stems are examples of agroindustrial residues studied, and yields of ethanol between 74.6 to 292.5 L per ton and costs between $0.58 and $0.76 per liter could be obtained.10 Besides, the conversion of grass and wheat straw into ethanol can reach yields up to 250 L per ton,11 and also cornstover can yield 227 L of ethanol per ton with costs up to $0.39 per ton.12 Colombia as an agroindustrial country presents a wide variety of products, which generate an important amount of agro-wastes. Many of these agroindustrial wastes are generated because of different reasons such as material rejection accordingly to quality requirements for crop products, end product characteristics (e.g., damage, ripeness and size of the fruit), as well as transportation © XXXX American Chemical Society

needs and sale prices. These residues usually are bagasse, straw, leaves or peel and seeds, and regarding to residues branches, sawdust and bark are the most typical. More than 27 million of wastes are produced every year: sugar cane bagasse represents 6 million tons, banana stem 227 thousand tons, Corncob a little more than 1 million tons, pineapple peel 150 thousand tons, mango wastes 66 thousand tons, palm oil wastes different to pulp (e.g., rachis) 840 thousand tons, plantain peel 417 thousand tons, rice husk 451 thousand tons, sawdust 33 thousand tons and wood bark 99 thousand tons, among other 28 agroindustrial residues which are produced in lesser quantity. The advantage of using agroindustrial residues in bioethanol production (second generation ethanol) is the reduction of both the environmental impact generated by fossil fuels and the wastage of the crop residues. On the other hand, the pretreatment used in the agroindustrial residues may generate a considerable environmental impact as the case of diluted acid pretreatment compared to hot liquid water.13 In this case, the reduction of the environmental impact is due to the transformation of hazardous components (e.g., acids) and other pollutants with low risk grade (e.g., salts).14 The chemical composition of agroindustrial residues is an attractive feature to obtain value-added products on a biorefinery concept. This can be reflected in the amount of cellulose, hemicellulose, and sugars which make them potential feedstocks for ethanol production (by breaking the polymeric matrix and its further transformation into simple sugars assimilated by fermenting microorganisms). Table 1 shows the average composition of the considered agroindustrial residues, which include the soluble sugars such as Received: September 3, 2014 Revised: January 1, 2015

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DOI: 10.1021/ef5019274 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels Table 1. Proximate Composition of Some Industrial Wastes agroindustrial waste

cellulose

lignin

hemicellulose

sugars

moisture

protein

ash

sum

sugar cane bagasse15−18 banana stem19 corncob15,20−23 pineapple peel24−26 peel, seeds, and whole mango24,27,28 other palm raceme wastes15,29−33 plantain peel34,35 rice husks15,36−40 sawdust41−46 wood bark15,47−50

49.96 44.93 32.10 6.04 5.20 7.15 1.41 36.89 25.89 27.90

22.90 4.70 12.84 0.37 4.70 8.76 1.49 19.77 14.92 12.59

15.20 17.01 31.91 5.89 3.70 23.98 1.58 16.08 16.67 13.75

1.81 20.85 1.65 0.82 5.77 0.52 0.23 1.42 1.57 3.30

9.50 8.34 8.30 84.07 69.00 60.00 87.40 5.40 38.00 40.00

0.91 0.89 4.59 1.23 0.86 6.88 1.14 2.27 2.93 0.66

1.1 2.8 6.0 1.3 0.2 2.1 1.3 16.3 2.4 2.9

101.38 99.52 97.39 99.72 89.43 109.39 92.03 98.13 102.38 101.1

sucrose, glucose, and fructose.15−50 The chemical composition of the lignocellulosic material provides an indicator on the feasibility to produce fermentable sugars as a function of the moisture content, cellulose and hemicellulose fractions. In this case, the moisture content is of great importance because it affects the effective raw material (dry matter), which can be transformed. The interactions of the components including ashes allow obtaining specific yields and sugars characteristics, as well as to determinate the efficiency losses, the amount of the inhibitor compounds (e.g., furfural and hydroxymethylfurfural) and the stillage characteristics after fermentation. The exposition of cellulose and hemicellulose polymers from the lignocellulosic matrix is achieved by means of a pretreatment process. The use of high temperatures in the pretreatment promotes the production of toxic compounds such as hydroxymethylfurfural (HMF) and furfural by the decomposition of hemicellulose and cellulose, and the transformation of glucose and xylose.51,52 For instance, acid pretreatment with concentrations ranging between 0.7−3.0% by weight has generated high solubilization and recovery of the hemicellulose allowing high levels of cellulose for its subsequent enzymatic hydrolysis.53 Once the lignocellulosic matrix is treated, specific enzymes are used to obtain sugars. The cellulose and hemicellulose exposed to active sites of the enzymes (cellulase, β-glucosidase, and hemicellulase) can be converted into sugars by breaking glycosidic bonds which are characteristic of these polymers. This prevents the degradation of the glucose and the resultant xylose.54 The ethanol production requires certain microorganisms that uptake glucose and xylose. Among them, xylose is significantly important because it is obtained at higher concentrations after the pretreatment and hydrolysis of the lignocellulosic material. On the other hand, in the fermentation step, it is possible to use two types of recognized microorganisms: Saccharomyces cerevisiae and Pichia stipitis, which consume mainly hexoses and pentoses, respectively. The high bioethanol demand and the estimated sales, around 34 million liters in December 2013,55 increase the importance to evaluate alternatives of production. Considering the descriptions presented before, the aim of this paper is to evaluate the technoeconomic and environmental feasibility of the fuel ethanol production from different agroindustrial residues in the Colombian context. Particularly, it evaluated the potential of 10 lignocellulosic materials, namely, the following: sugar cane bagasse, banana stem, corncob, rice husk, sawdust, woodbark, mango wastes, palm residues, pineapple peel, and plantain peel. Accordingly, this procedure is done using a simulation approach based on experimental proximate composition of these materials. Then an analysis is considered from the technical, economic, and environmental points of view for the processing facility.

2. PROCESS MODELING 2.1. Process Description. Known schemes of ethanol production from literature were used in the simulation.9,12,56 Anhydrous ethanol production considered stages such as of acid pretreatment, enzymatic hydrolysis, fermentation with P. stipitis and S. cerevisiae, distillation, and dehydration with molecular sieves (see Figure 1). The process yields were evaluated based on material and energy balances generated by simulation (using Aspen Plus) for a feed rate of 1000 kg/h of raw material. Table 2 shows units, conditions and main streams of the process (For further specifications, see Supporting Information). The pretreatment consisted of the milling of the raw material to 1 mm of particle diameter, then the material was exposed to acid, ultrasonic, and thermal treatment.57,58 Sulfuric acid diluted to 3% v/v with a solids/acid ratio of 1 to 4 was used (i.e., a solid load between 49.8 kg/h and 908.6 kg/h of lignocellulosic material and insoluble protein depending on the raw material), and the obtained mixture was exposed to sonic intensity with a frequency of 37 kHz, 80 °C, and a residence time of 1 h. The thermic effects were applied by autoclaving at 121 °C and 20 bar during 1 h.59,60 Additionally, in order to optimize the acid use, the treated stream is recirculated. Then, the liquid effluents are neutralized with diluted NaOH, and the salt formed is separated. Moreover, NaOH is used because of its low cost, and the Na+ helps to expose the active sites of the cellulolytic enzyme.61 Pretreatment and hydrolysis yields were considered on the basis of kinetic models from the literature62,63 to evaluate the xylose and glucose degradation and the HMF and furfural production (see Supporting Information for a short example). Furthermore, due to the salts and minerals present in the feedstock, an acid neutralization step was included. After the pretreatment step, the liquid and solid phases were separated by centrifugation. The xylose-rich liquid phase from hemicellulose was detoxified with activated charcoal at 45 °C for 1 h and a concentration of 50 g/L. Moreover, the cellulose-rich solid phase was transformed into hexoses (mainly glucose) using cellulase and beta-glucanase. The process is carried out by mixing the solid with water (ratio 1:4) at temperatures between 50 and 60 °C, atmospheric pressure and pH 3.5−5.0. Also a buffer solution of sodium citrate for enzymes was considered. The fermentations considered an average ethanol yield from S. cerevisiae and P. stipitis in two parallel reactors. Commercial strain of Saccharomyces cerevisiae ATCC 9763 ATCC − ARS Culture Collection from ANNAR was considered. It was performed taking into account the nutrient consumption per 1 g of sugars as follows: 2.2 mg of sodium phosphate, 6.3 mg of urea, 0.0175 mg of ferric chloride and magnesium sulfate. Temperatures between 30 to 35 °C and reaction times up to 48 h were considered in this step. The separation of solid and liquid phases was only B


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Figure 1. Process streams for ethanol production from lignocellulosic material: 1. mill, 2. mixing tank with sulfuric acid 3%, 3. ultrasound stage, 4. thermic stage (autoclave), 5 neutralization tank, 6. centrifuge, 7. activated charcoal tank, 8. mixing tank with water, 9. hydrolysis stage, 10. xylose fermenter, 11. glucose fermenter, 12. centrifuge, 13. distillation tower, 14. rectification tower, 15 molecular sieves, 16. pressure tank.

Table 2. Stream Summary equipment

conditions

input kg/h

component

output kg/h

component

disc mill mixing tank ultrasound autoclave neutralization centrifuge 1 detoxification xylose fermenter hydrolysis stage glucose fermenter centrifuge 2 distillation tower rectification tower molecular sieves

49% 1000 um 25 °C 80 °C 121 °C, 20 bar 50 °C 1000 rpm 45 °C 30 °C 50 °C 35 °C 25 °C 1 atm 1 atm 116 °C, 1 N/m2

1000 125.98−809.99 129.31−2620.62 0.02−0.39 0.95−36.31 12.63−262.70 2.41- 49.97 12.63−262.31 15.68−500.25 15.30−488.04 12.72−283.68 3512.88−4170.13 2.90−60.44 1.04−28.48

plantain peel 3% acid fiber furfural acid xylose HMF xylose cellulose glucose ethanol water water water

990 8.85−127.44 52.27−1535.94 0.03−0.55 0.01−0.30 12.63−262.31 2.21−45.97 5.47−113.66 15.30−488.04 7.24−231.18 12.71−283.29 1.88−36.29 1.04−28.48 0.02−0.57

plantain peel acid cellulose furfural base xylose HMF ethanol glucose ethanol ethanol water water water

plantain peel. Table 1 shows the average composition of the raw materials used in the simulation procedure. These compositions are normalized in order to accomplish the requirements for the mass and energy balance calculation in the simulator and proportionally distribute the effect of raw material components in the process. Most of the referenced authors determined these compositions according to National Renewable Energy Laboratory (NREL), which allows a proper comparison between residues. The moisture content could change slightly with the weather seasons; however, moisture content was considered constant throughout the year because it was a tropical region without seasons. The moisture amount of every residue is affected by typical mechanical and physico-chemical processes used during transformation steps of main raw materials. For example, the banana stem (found in harvest area) and sugar cane bagasse can be exposed to sunlight when these repose on the field, reducing the moisture compared to other residues which are extracted from harvest or processing as the plantain peel. The results obtained for the ethanol yields in the simulation were compared with the experimental results presented in literature with similar processing steps (i.e., acid hydrolysis, S.cerevisiae and P. stipitis fermentation).

considered until the completion of the reaction. Then, ethanol is concentrated by a distillation tower allowing an average concentration of 80%. Later, ethanol at 90% is obtained through a rectification tower. Finally, ethanol is dehydrated and concentrated at 98 and 99% using molecular sieves.64 2.2. Simulation Procedure. To evaluate the fuel ethanol production from lignocellulosic residues, flowsheet synthesis was carried out using process simulation tools. The objective of this procedure is to generate the mass and energy balances in order to calculate raw materials requirements, consumables, utilities, and energy needs. The main simulator used in this study was the Aspen Plus v7.3 (Aspen Technology, Inc., U.S.A.) package allowing the evaluation of each process stage. Also, Aspen Plus considers both physical and chemical transformations. First, the feedstock composition is considered, finding the approximate thermodynamic characteristics and predicting changes and interaction of the generated phases. The nonrandom two-liquid (NRTL) model was used for the liquid phase and the Redlich− Kwong (RK) model for the gas phase. The evaluation of ethanol production was carried out for sugar cane bagasse, banana stem, corncob, rice husk, sawdust, woodbark, mango wastes, palm residues, pineapple peel, and C


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Energy & Fuels The agroindustrial residues have several origins depending on the final use of the derived products. Some residues can be found in the harvest area, including the wood bark and banana stem. Other residues such as corncob, rice husk, and sawdust are derived from the productive processes. The sugar cane bagasse is obtained from the industrial crusher once the juice is extracted. Moreover, there are other residues such as plantain peel, pineapple leaf, and mango, which were used after additional processes as peeling, crushing, and heating, among others. These additional processes are applied to obtain a variety of added-value products not being a direct waste result of basic human consumption practices or agroindustrial processing. The generation contexts of the residues can produce high differences in the composition, and hence, these compositions cannot be compared with each other. However, once the residues are obtained, the qualitative composition usually does not change (authors normally use similar components quantification rules). Nevertheless, in this work, data were calculated on the basis of an average of at least three references for each residue to ensure a good qualitative verification and consistency to reduce composition deviations. It should be noted that in the present work this approach is just a normal approximation to solve one of the most important problems in residues use: composition variability as a result of origin, context, and practices. (For example, if residue storage varies just in terms of time, the cellulose or starch can be degraded by different microorganisms, and the overall composition can decrease in this compound while sugars can increase.) 2.3. Economic Analysis. This work presents annualized production cost of ethanol production (sale price of $0.93 per liter). Process simulation generates both the energy consumption and the utilities requirements of each stage, as well as the characteristics of some equipment. These data were used to calculate the corresponding ethanol production cost using the commercial package Aspen Process Economic Analyzer. The equipment costs were re-evaluated using reported industrial cost from builders, sellers, simulator software database, and updated equipment data. Utilities costs were calculated using the present value (local value based on data in the west center of Colombia) for industrial water and fuel (i.e., natural gas) for steam generation. The agroindustrial wastes costs were calculated for industrial demand considering the transport logistics and labor costs. Also other raw material costs such as acids, bases, enzymes and minerals were considered for industrial demand of 24 tons per day. The profit margin was calculated by (profits − total cost of raw material and utilities). Table 3 shows the raw material costs for the different agroindustrial residues. In addition, it is very important to consider that the evaluation was carried out according to the Colombian conditions. The economic evaluation was developed for an ethanol production between 99 900 and 2.8 million liters per year (depending on the raw material). 2.4. Environmental Analysis. 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 to calculate the potential environmental impact (PEI). This method proposes to add a conservation reaction over the PEI based on the impact of input and output flow rates from the process. For this application, the EPA developed the software WAR GUI. The PEI for a given mass or energy quantity could be defined as the effect that those (energy and mass) would have on the environment if they are arbitrarily discharged. The environmental impact is a quantity that cannot be directly measured. However, it can be calculated

Table 3. Prices Used in the Economic Evaluation item

price

units

watera natural gasb sulfuric acidc,d sodium hydroxidec,d sodium phosphatec,d ureac,d ferric chloridec,d magnesium sulfatec,d cellulasee ethanolf

0.90 0.50 0.28 0.53 2.00 0.82 45.9 0.43 1.0 1.27

USD/m3 USD/m3 USD/kg USD/kg USD/kg USD/kg USD/kg USD/kg USD/kg USD/kg

a Tariff for industrial water demand (local water supplier). bUnidad de planeación minero energética (Ministry of Energy in Colombia). c Prices based on ICIS pricing indicatives. dPrices based on SigmaAldrich. ePrices based on Alibaba international prices. fFederación Nacional de Biocombustibles de Colombia.

from different measurable indicators.65−68 The WAR GUI software incorporates the WAR algorithm in the process design measuring eight categories as follows: 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 both mass effluents and energy requirements of a chemical process based on the energy and mass balances generated in the Aspen Plus calculations into the chemical process. Therefore, the calculations do not consider the LCA analysis from raw material production up to final product distribution. Then, the weighted sum of all impacts ends in 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. This analysis serves as a basis to compare different processing configurations.

3. RESULTS AND DISCUSSION The potential ethanol production of the residues considered yields, techno-economic, and environmental results. Tables 2 and 4 condense the results obtained by the authors, and all figures also are part of the results obtained by the authors in this work. At least six agroindustrial wastes show promising yields up to 0.170 kg of ethanol per kg of feedstock. Table 4 shows the ethanol production yields for the 10 raw materials considered in this paper. Yields obtained by process simulation were compared with experimental yields,69−80 which considered acid pretreatment, enzymatic hydrolysis, and the use of S. cerevisiae in the fermentation stage. Although the protocols of transformation change slightly for each raw material, the yields show a good proximity to experimental results. This difference is because the amount of acid, temperatures, pressure, and salt concentration among other variables. Furthermore, the experimental yields showed the effectiveness of the simulation projection with a MMRE (Mean Magnitude of Relative Error) of 0.238. The highest yield obtained corresponds to banana stem (0.26 kg of ethanol/kg feedstock), followed by sugar cane bagasse and rice husk with 0.255 and 0.257 kg ethanol/kg feedstock, respectively. These yields are mainly due to its low moisture content and a high cellulose content (37% approximately) (see Table 1). The high cellulose content enhances the possibility to obtain appreciable amounts of glucose after hydrolysis whether the D


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equilibrium with a xylose yield of 0.1 kg per kg of raw material and a glucose yield of 0.46 kg per kg of raw material. Furthermore, the HMF and furfural concentrations can lead to a complete inhibition of the microorganism and generate damage in the cell growth.80,81 For instance, for S. sereviciae concentrations up to 4 g of furfural per L and 8 g of HMF per L generate 79% and 50% of inhibition, respectively.82,83 A furfural concentration of about 1.3 g/L gives inhibition of 9% for P. stipitis. Consequently, the inhibition by pretreatment justifies the detoxification with activated charcoal reducing the toxic components between 75% and 95%.82,84,85 The variety of conditions and differences from an operational point (on the referenced experimental results) produce comparative limitations for acid pretreatment, which were developed in a temperature range between 100 and 180 °C and with different acid concentrations. This occurs principally because of the recalcitrant characteristics of the lignocellulosic material that change depending on the agroindustrial residues used. Nevertheless, the known procedure adopted for the simulation shows an important proximity to the experimental results, indicating that there is a possibility to generate a flexible process for a variety of residues. On the other hand, the material losses in the process reached 1% during the milling stage. These losses are an important factor because they can sum up to 7%, affecting considerably the processing yields. 3.1. Equipment Cost. One theoretical process for ethanol production was proposed, being in most of the cases the same equipment for all the agroindustrial wastes. Fixed costs for equipment are distributed according to Figure 2. The higher

Table 4. Agroindustrial Residues Costs, Simulation Yields, and Experimental Yields Reporteda raw material cane bagasse banana stem corncob pineapple peel mango wastes other palm wastes plantain peel rice husk sawdust wood bark a

total cost (USD/ton)

simulation yield (kg Et/kg RM)

$10.62 $17.32 $14.28 $14.13

0.255 0.259 0.253 0.049

0.3−0.1868 0.20−0.1569 0.43−0.3170 0.03371

$61.08 $17.57

0.034 0.108

0.06172 0.11873

$27.09 $34.21 $49.73 $49.73

0.009 0.257 0.168 0.166

ND 0.13−0.2574−77 0.3−0.0978 0.18−0.1179

experimental yield

ND: not defined. RM: raw material. Et: ethanol.

polymeric matrix is properly exposed, which increases the ethanol production in the fermentation. The lignin content generates greater limitations to expose the cellulose from bagasse in comparison with banana stem. Consequently, although the cellulose content in the banana stem is lower, the yields reached are similar to the one obtained from sugar cane bagasse. The hemicellulose content in these two residues exceeds 15%, which is diluted during the acid treatment and then is broken, exposing the xylose into the liquid medium. The generated xylose corresponds to 126.768 kg xylose/h in the case of the banana stem. However, the residue with the highest xylose production was corncob with 262.313 kg/h. Considering that xylose is fermented with P. stipitis, corncob showed one of the highest ethanol production yields (0.253 kg ethanol/kg feedstock) reflecting the potential of this raw material for ethanol production. Conversions around 72% were achieved in the pretreatment stage, which are not higher because of the difficulty to totally access to hemicellulose in the lignocellulosic matrix. Once the cellulose is isolated, the hydrolysis step showed conversions of 97%, and these were the highest of the three stages. This is due to the enzymes’ specificity and the good effect of pretreatment exposing effectively the pulp. In the fermentation stage, moderate yields of 46.5% were achieved mainly due to the metabolism of microorganisms and the presence (traces) of toxic compounds that affect mostly the microorganisms in comparison to enzymes. The higher cellulose and hemicellulose contents may represent the highest ethanol yields, but it is necessary to consider the effect of the other components in the exposure of these two biopolymers. For instance, the lignin generates a limit resulting in differences as reflected in yields (see corncob and cane bagasse). Also, ashes involved neutralizing the acid in the pretreatment stage reducing its effectiveness. The agro-wastes that generated the lowest ethanol yields were banana peel, mango waste, and pineapple peel because of their high moisture content (above 70%) and their low cellulose and hemicellulose concentrations (less than 6%). On the other hand, the HMF production ranged from 2.2 g/L to 39 g/L for the 10 evaluated processes. Besides, 0.13 g/L of furfural due to degradation of the high glucose content in some materials and xylose from hemicellulose was obtained. Xylose and glucose are strongly affected by temperature and acid concentration, in other words, the severity of the pretreatment.81 The xylose yield is between 0.01 to 0.21 kg per kg of raw material, and the glucose yield is between 0.15 and 0.49 kg per kg of raw material. According to the sugar yields, the banana stem has the better

Figure 2. Percentage of participation in the cost of equipment according to process stages.

costs were those for the separation equipment with $2.15 million representing 50% of the capital costs with the distillation towers and the dehydration stage contributing with more 1.4 million dollars. The costs of pretreatment equipment were 27% of the total capital cost with $1.18 million. The pretreatment equipment costs consisted of milling, mixing tanks, heat treatment reactor, acid treatment reactor, agitated tank for neutralization, centrifuge, detoxification reactor with activated carbon, among others, with the centrifuge and the mill the most expensive. The equipment in the transformation stage (hydrolysis and fermentation) represent costs around $0.99 million of a total of $ 4.3 million dollars. The modeled process can be scaled up to industrial application when the raw materials with lower moisture and higher performance for ethanol production are considered. This is E


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(see Figure 4). Nonetheless, mango wastes, palm wastes, pineapple peel, and plantain peel showed unfeasible economics. Sugar cane bagasse, banana stem, corncob, and rice husk generate profits over $2,000 daily and up to approximately $3,000. Moreover, with respect to the processing costs, Figure 5

because the equipment is already commercially available. Nevertheless, it is necessary to achieve a profitability in each process step (e.g., the cost of diluted acid cannot overcome the earnings from the sales of ethanol in Colombia) in order to ensure a positive economic margin industrially. 3.2. Utilities Costs. Figure 3 shows the approximate cost for utilities, which considers water cost for heating (0.78 dollar per m3)

Figure 5. Total cost of raw material and fluids of process for 1 ton/h of wastes to ethanol.

shows the daily costs for both reagents and energy. Although rice husk do not have the maximum ethanol production yield, this is enough to generate the highest profits due to its low cost in comparison to the other three residues with similar yields (palm wastes, sawdust, and wood bark). This is a result of the relationship between the raw material costs, the dry material available as well as the utilities cost, which are 46% lower than the cost required for banana stem. Raw materials and utilities reached on average 54.7% and 45.3% of the total variable process costs, respectively. This demonstrates the impact of energy on economics exposing that there is a need for further consideration of heat exchange networks to reduce external consumption of energy.88 Likewise, the Agro-wastes cost reaches 32.8% of the reagents cost and approximately 18% of the total cost per day of materials to develop the process. Table 4 shows the raw material cost per ton for a demand of one ton per hour of ethanol. The cost considers the logistic for the residue in each specific production region of Colombia (Valle del Cauca, Antioquia, Córdoba, Santander, Tolima, Meta and ́ The expensive raw materials costs are due to the Quindio). difficulties inherent to their transportation, freight, and other factors such as labor in each region. In all the cases, ethanol production processes must be located in the corresponding region. The enzyme cost can correspond to the 30−60% of raw material costs. Thus, ethanol production is very sensitive to the enzyme availability at a suitable price and sufficient profitability. For demands greater than 1.5 ton of enzyme daily, the prices can reach up to $1/kg. 3.3. Environment Impact Results. The environmental impacts of the process and the raw material utilization were determined. Figure 6 shows the environmental impact per hour for feedstock streams directly when these are not utilized and the environmental impact due to their use for the ethanol production. Additionally, Figure 6 presents the environmental impact per kg of product (ethanol), showing that environmental impacts due to not use of the residues and other raw materials (e.g., acids, bases, and minerals) for the processing is diminished when these are considered and transformed into ethanol. In all the cases, it is considered an average reduction of 39% of the environmental impact due to the use of the residues and raw materials involved. Likewise, the highest environmental impact reduction is presented by the banana stem with a 59% and the lowest is presented by the palm waste (excluding pulp) with 19%.

Figure 3. Cost of service fluids required for heating and cooling.

and cooling,86 and cost of natural gas ($0.47 dollar per m3)87 for steam production. These utilities represent an average water consumption of 141 m3 per day and up to 202 m3 per day and about 3482 m3 of natural gas with a maximum requirement of 4899 m3 per day for corncob transformation. The differences between the utilities costs are generated by the amount of ethanol and water in the mixtures to be separated and the energy associated with this separation. The amount and proportion of flow (ethanol−water), and the reflux ratio of water in the system are considered to evaluate the energy demand. Generally wastes with a high ethanol yield and moisture of approximately 10% require higher energy inputs (e.g., other palm raceme wastes and pineapple peel). The latter is because the proportion of solids in the process needs an important amount of water the dilutions, and the extraction of this water in the separation is energy intensive. Otherwise, the utilities costs are not less than $ 700 per day, and do not exceed $ 2,500 in most of the cases (see Figure 3). Hence the average water cost required is $126 per day, 14 times lower than the fuel cost to full fill heating requirements. Figure 4 shows the profit margins for each residue. Not all the agroindustrial wastes are attractive for ethanol production. The

Figure 4. Profit margin of the ethanol process from agroindustrial wastes in Colombia.

obtained profit margin depends on ethanol yields, and in this case, six residues generate a positive profit margin: cane bagasse, banana stem, corncob, rice husk, sawdust, and woodbark F


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Figure 6. General PEI of the process and reduction by use of raw materials.

Figure 7. General PEI with specific categories.

Figure 8. Environmental impacts of the process and the energy consumption.

The use of sulfuric acid in the pretreatment and the sugar production generate a diluted undercurrent as result of the ethanol concentration. This effluent increase the organic charge

in the water and produce a disproportionate increment of microorganism and algae. This eutrophication decrease the dissolved oxygen, and the system can be anoxic. Nevertheless, the G


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unused material and transformation of the wastes used as raw material can produce a higher organic charge as a result of the production of an important oxygen demand by the untransformed components. Only about 3% of the initial sugars ends up as byproduct and with trace minerals and sulfates can be recycled as compost material for easy assimilation in crops. Figure 7 shows specific aspects contributing to major environmental impact. The environmental impact values in the case of agroindustrial waste are strongly affected by the chemical oxidation potential due to the ability of the organic material and some compounds in the process of reacting in water, which reduces the amount of available dissolved oxygen. Figure 8 shows the contribution of the energy consumption in the total environmental impact for ethanol production. The average contribution of energy consumption in the environmental impact is 4.3%. The corncob contribution to the ethanol process has the highest environmental impact relative to energy (8 PEI/h), which is consistent with the fact that this is the process with the highest energy consumption.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: + 576 8879300 Ext: 50199. Fax: + 57 6 8879300 Ext: 50452. Notes

The authors declare no competing financial interest.

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REFERENCES

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4. CONCLUSIONS The industrial processes were investigated through simulation, which is based on known conditions, yields, experimental standardized compositions, and tested stages, and allowed us to project the ethanol production from industrial wastes. Therefore, effluents, wastes, and product performance throughout the process can be evaluated approximately. This allows an understanding of the possibility or impossibility of obtaining ethanol profitably. Moreover, simulations provide additional data such as energy requirements and allow the assessment of the characteristics of the equipment and their related costs. This predicts the possibility of using agroindustrial wastes, specifically for the conversion into ethanol with respect to enzymatic saccharification. The latter also serves as a global view in terms of sustainability concerns of different biofuel systems. This work has allowed the consideration of cane bagasse, banana stem, corncob and rice husk as raw materials with real potential for ethanol production. These wastes showed profitability higher than 40% with respect to the process flows (reagents, raw materials, fuel) and reduction of the environmental impact of 44% on average. However, mango wastes, palm wastes, pineapple peel, and plantain peel, because of their negative profitability and low reduction of the environment impact compared to other wastes, do not show potential to be used in ethanol production. The poor and nonexistent profitability of these agro-wastes is strongly linked to its high moisture content and low cellulose and hemicellulose contents. This study demonstrated the possibility to obtain economically feasible biofuel using acid pretreatment, detoxification, enzymatic hydrolysis of cellulose and fermentation of pentoses and hexoses (using P. stipitis and S. cerevisiae), ethanol separation by distillation and rectification column, and ethanol concentration by molecular sieves.

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S Supporting Information *

Additional information as noted in text, including summary of simulation data, sample calculations, and description of the model. This material is available free of charge via the Internet at http://pubs.acs.org. H


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Energy & Fuels

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Techno-Economic and Environmental Analysis of Ethanol Production

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