As mentioned in Case Study 11.1, the aim of simulating the process to obtain bio­ethanol from dry-milled corn can provide valuable information on the suitability of different technological configurations, in this case, the configuration involving the SSF of the starch contained in the corn grain. This simulation was performed in previous works (Cardona et al. 2005b; Quintero et al. 2008) under Colombian conditions and corresponds to the scheme depicted in Figure 11.8. As in the case of cane-to-ethanol conversion, the downstream processes are practically the same (see Case Study 11.1). The differences lie in the biological transformation step. After washing, crushing, and milling the corn grains (dry-milling process), the starchy material is gelatinized in order to dissolve the amylose and amylopectin.

In dissolved form, starch is accessible for enzymatic attack in the following liq­uefaction step. In this step, a partial hydrolysis (about 10%) of the starch chains using thermostable bacterial a-amylase is achieved. The hydrolyzate obtained has reduced viscosity and contains starch oligomers called dextrins. Then, the liquefied starch enters the SSF process where it is hydrolyzed by microbial glucoamylase to produce glucose. This sugar is immediately assimilated by the yeast S. cerevisiae in the same reactor and converted into ethanol. As mentioned above, the dry-milling technology allows the production of DDGS using the recovered solids from the bot­toms of the concentration column. In this case, no co-generation is contemplated;

thus, the acquisition and utilization of fossil fuels to supply the steam for the pro­cess are required.

The simulation of this process was carried out using Aspen Plus as well. Main input data employed for process simulation are shown in Table 11.7. As in the case of cane ethanol, the simulation used a production capacity of about 17,830 kg/h anhydrous ethanol. The simulation approach described in Chapter 8, Case Study 8.1 and others was also applied for this case study. Enzymatic hydrolysis and con­tinuous fermentation processes were simulated based on a stoichiometric approach that considered the conversion of starch into glucose as well as the transformation of glucose into cell biomass, ethyl alcohol, and other fermentation by-products. The economic analysis was performed by using the Aspen Icarus Process Evaluator package and the same local conditions of Case Study 11.1.

Some simulation results of main streams for this process are shown in Table 11.8. The compositions of the streams calculated by simulation agree very well with those reported for commercial processes. The DDGS generally contains 9% moisture and 27 to 32% protein (McAloon et al., 2000). Results of many analyses done during a five-year period (1997 to 2001) to determine the composition of DDGS obtained in corn dry-milling ethanol production facilities in the United States (Belyea et al., 2004) revealed good agreement with the simulation data obtained (see Table 11.8). The results obtained for ethanol yield in the process analyzed, along with total operating and capital costs, are shown in Table 11.3. In this regard, the average

yield of technified corn crop in Colombia is about 5 ton/ha for a harvesting time of four months (Quintero et al., 2004). This yield is lower than the corn yield in the United States, the major corn producer in the world. Note from Table 11.3 that the calculated ethanol yield from corn (in terms of produced ethanol per tonne of feedstock entering the plant) is greater than that from sugarcane because of the higher amount of fermentable sugars (glucose) that may be released from the origi­nal starchy material. However, the annual ethanol yield from each hectare of cul­tivated corn is 23.6% lower than that for sugarcane. This preliminary fact shows the comparative advantage of using sugarcane as feedstock for ethanol production under high-productivity conditions for cane cropping in Colombia.

Total operating costs are significantly higher for ethanol production from corn than from sugarcane under Colombian conditions (Table 11.3). This is mostly explained by the feedstock cost, as shown in Table 11.4, where operating costs were

TABLE 11.7

Main Process Data for Simulation of Fuel Ethanol Production from Corn

Source: Quintero, J. A., M. I. Montoya, O. J. Sanchez, O. H. Giraldo, and C. A. Cardona. 2008. Energy 33 (3):385-399. Elsevier Ltd. With permission. a All the percentages are expressed by weight.

TABLE 11.8

Flow Rates and Composition of Some Streams for Corn-Based Ethanol Process

Source: Quintero, J. A., M. I. Montoya, O. J. Sanchez, O. H. Giraldo, and C. A. Cardona. 2008. Energy 33 (3):385-399. Elsevier Ltd. With permission.

disaggregated. In comparison to corn, the greater cane demand for producing the same amount of ethanol (about six times the grain requirements) is compensated for by the lower cost of this raw material. In fact, the main part of fuel ethanol costs corresponds to the feedstock: 66.45% and 70.84% using sugarcane and corn, respectively. Usually the feedstock cost for Brazilian cane ethanol is about 60% of the production costs (Xavier, 2007), whereas for corn (mostly transgenic) the cost is about 63% in the United States (McAloon et al., 2000). These results confirm the validity of the data obtained by simulation, as well as the assumptions considered during the economic analysis. Steam and power generation through the combustion of cane bagasse reduces the utilities cost considerably. This makes a big differ­ence in cane-to-ethanol processes and justifies the installation and operation of bagasse combustion systems. On the contrary, a corn-based process requires the consumption of fossil fuels that negatively affects its operating costs and environ­mental performance. Total capital costs are lower for the corn process (Table 11.3), even though it has a more complex configuration involving an additional enzymatic hydrolysis step. For the cane-based process and due to the higher amount of feed­stock to be handled, a greater capacity of equipment is required. In addition, the co-generation system increases the required investment for such types of configura­tions. Nevertheless, the possibility of selling electricity contributes to offset these additional expenses.

The production costs structure of one liter of ethanol produced from corn (as shown in Table 11.4) is comparable to the costs structure estimated by the NREL for the mature process of ethanol production from corn via dry-milling technology in the United States. In the latter case, ethanol production costs reach US$0.232 per liter of anhydrous ethanol (McAloon et al., 2000). The main difference in the production costs are mostly explained by the higher corn prices in Colombia related to cheaper U. S. corn (0.076 US$/kg). In fact, the utilization of imported corn from the United States as feedstock for new ethanol production facilities located on the Colombian Caribbean coast has been proposed by different orga­nizations including the Colombian government. In any case, the volatility of corn prices is a crucial factor to be accounted for. Production costs obtained in this case study are very close to those reported by Macedo and Nogueira (2005) for ethanol production from milo (a kind of sorghum) in the United States. It should be noted that co-product (DDGS) sales in corn ethanol production play a significant role in process sustainability.

The confirmation of the economic viability of the two analyzed processes is presented in Table 11.5. In relation to their profitability indicators, both processes are comparable although the production costs for sugarcane are clearly smaller. Moreover, ethanol from cane offers a higher NPV with a lower internal rate of return (IRR). Different evaluations simulating changes in the price of the main feedstock show that the process employing sugarcane is much more stable to these kinds of variations. Thus, a 71% increase in the price of corn (likely to occur under Colombian conditions) leads to negative NPV during the lifetime of the project. In contrast, this same increase in the price of sugarcane (whose price is much more stable in Colombia) only leads to a 38.7% reduction in NPV and 28.4% decrease in IRR. These results allow the conclusion that ethanol pro­duction process from sugarcane represents the best investment possibility under Colombian conditions.