Using the simulation results from the last case study, the environmental assessment of bioethanol production from dry-milled corn employing the SSF process was car­ried out applying the WAR algorithm (Quintero et al., 2008). The results obtained were compared to the outcomes corresponding to the process for ethanol produc­tion from sugarcane.

The total output rate of environmental impact for corn ethanol process is shown in Figure 11.3 and the potential environmental impact generated within the sys­tem is shown in Figure 11.4. It is evident that ethanol production from sugarcane has also lower impact on the environment compared to the corn process: the latter process exhibits a higher PEI per mass of products. This environmental indicator is the index to be considered for the overall evaluation of both processes. On the other hand, the corn-based process has a more negative generated PEI meaning that the PEI of the substances entering the system is reduced by their transforma­tion into other less dangerous compounds. For the sugarcane process, the higher value of generated PEI (although negative) indicates that the conversion of entering substances also occurs, but to a lesser degree. This feature can be related to the fact that this process requires the input of a greater amount of feedstock. The commer­cialization of DDGS implies the elimination of the polluting stillage stream.

The four case studies presented above demonstrate the power of the simula­tion approach employed. In order to obtain a general framework for comparing different technological configurations for ethanol production from diverse feed­stocks according to technoeconomic and environmental criteria, the formulation of an overall comparison criterion is required. This is shown in the following case study.

Case Study 11.5 Combined Evaluation of Technoeconomic and Environmental Performance of Two Bioethanol Production Processes

The evaluation considering economic and environmental indexes of the two simu­lated processes for ethanol production from sugarcane and corn under Colombian conditions was carried out to obtain a combined index useful for the selection of the most appropriate technology as formulated in previous works (Quintero et al., 2008; Sanchez, 2008). This combination was done by following the procedure developed by Chen et al. (2002) where the economic and environmental objec­tives were aggregated into a single objective function using the analytic hierar­chy process (AHP) approach. The AHP is one variant of multicriteria analysis that uses a number of pair-wise comparisons between quantitative or qualitative crite­ria to assess the relative importance of each criterion. These comparisons can be arranged in a hierarchical manner to form sets of attributes, and qualities (levels) within these attributes (Hussain et al., 2006).

The hierarchical structure for this case study is shown in Figure 11.9. Once mass and energy balances have been calculated by simulation, the economic and environmental evaluations are performed by using the corresponding tools (process

evaluation software and WAR algorithm, respectively). The indexes (NPV and PEI) for each process are determined from these evaluations. Alternatively, other eco­nomic indexes, such as IRR, can be used. The indexes are normalized, so that they do not exceed a normalization value, and converted to quantitative scores. The normalization value for each index was calculated as the sum of the index values in both processes. The economic score of a given process was determined as the ratio between the NPV of the process and the corresponding normalization value, i. e., the sum of the two calculated NPVs. The environmental score was calculated tak­ing the difference between the corresponding normalization value and the PEI of the process and dividing the result obtained by the normalization value. The AHP score of a process design represents the sum of the products of the average process score for a given attribute and the weighting for that attribute, that is:

AHP = PEcn ■ weight + PEnv ■ weight (11.1)

where PEcn is the normalized economic score calculated from the NPV of the two analyzed processes, and PEnv is the normalized environmental score resulting from the PEI values of the two processes. The qualitative weightings of economic and environmental attributes were taken as 0.82 and 0.18, respectively. These values are suggested by Chen et al. (2002) who applied them to several chemical processes based on the survey carried out by Dechanpanya (1998) on the comparison of eco­nomic and environmental attributes for a chemical process from several faculty members and graduate students at Michigan Technological University using the AHP approach (Hussain et al. 2006).

The results obtained from the integration of the economic and environmental assessments following the proposed methodological approach are presented in Table 11.9. From these results, the sugarcane-based process exhibits a higher AHP score, indicating that it has better performance than the corn-based process when both economic and environmental criteria are analyzed in a combined manner. Changes in the evaluation of the AHP when different weightings are selected for both processes show that the sugarcane-based process will always have a better

performance with any value of the economic weighting. When this weighting is increased (this is equivalent to the reduction of the environmental weighting), the AHP score will also increase. In contrast, the corn-based process shows slightly worse performance when the economic attribute has a higher weight. This means that the economic advantages for the sugarcane process are actually better than those displayed for the corn scenario. Therefore, assigning weightings for this case study does not affect the final qualitative result of the combined evaluation within the AHP framework. Thus, the process that employs sugarcane for producing fuel ethanol shows better performance.

The procedure proposed to analyze different flowsheet configurations proved to be a useful methodology for process synthesis and can support the decision making for further experimental studies at pilot scale and industrial levels. This is a key issue considering the limited resources for extensive and long-term research in such countries as Colombia.