The assessment of fuel ethanol production processes from an environmental point of view implies not only the quantification and analysis of the polluting burdens generated by the conversion technologies, but also the evaluation of the environ­mental performance of such processes considering their influence on the planet in terms of the depletion of natural resources, contamination of the ecosystems, and global environmental impacts generated.

The starting point to satisfy the standard of clean production is the environmen­tal diagnosis in order to determine the opportunities for preventing or reducing the contamination sources and the viable alternatives to carry out such reduction. In the last four decades, the design of processes and the production of chemicals have experienced a great evolution (Montoya et al., 2006). Initially, reaction and separation systems were designed and optimized according to only one economic objective. In the 1970s and 1980s due to the global energy crisis, the utility sys­tem (thermal energy, power, cooling water) was included in the process design and optimization procedures. Nowadays, under the clean production scheme, the environmental objective should be taken into account along with the economic objective. In this sense, the environmental objective should be to consider not only the specific environmental impact of the process, but also its impacts in other steps of its life cycle.

The waste minimization as a means to attain the clean production and con­tribute to the sustainable development has been extensively studied in the process industry and in academic circles. The waste minimization incorporates both the waste reduction in the source and the use of recycling to reduce the amount and risk of the residues. Nevertheless, the difference between hazardous and nonhaz­ardous wastes is not considered. In this connection, the minimization of the envi­ronmental impact is a much stricter norm and, although its scope is similar to the waste minimization, emphasizes in a higher degree the different impacts of the chemical species on the environment (Yang and Shi, 2000). The measurement of the environmental performance of a process can be viewed as a decision problem involving two levels: screening indices on the process level and environmental performance indicators on the chemical species level. The latter indicators are the base of the screening indexes and offer enough flexibility to consider the fate of all the components involved and their subsequent impacts. Such impacts are in the framework of a group of categories, i. e., environmental performance indi­cators show the impact with which the process contributes to a given category. Among these categories, the following should be highlighted, as reported by Yang and Shi (2000): energy consumption, resource consumption, greenhouse effect, ozone depletion, acidification, eutrophication, photochemical smog, human toxic­ity, ecotoxicity, area used and species diversity, odor, and noise. To consider the environmental impact of a process, several methodologies have been developed, such as the life cycle assessment (LCA) and the waste reduction (WAR) algo­rithm, among others.

10.2.1 WAR Algorithm

In Chapter 2, Table 2.1, the different steps involved in the development of an industrial process during its life cycle were highlighted. During the conceptual process design step, the information available is quite limited and uncertain. In this case, the screening indexes and environmental performance indicators should be based on simple mass and energy balances. Thus, the environmental indicators developed by Heinzie et al. (1998) that take into account the different decision levels during the conceptual design are based on mass losses. In a similar way, the ecotoxicological models developed by Elliot et al. (1996) are also based in mass units instead of concentration units (Yang and Shi, 2000).

The waste reduction algorithm originally proposed by Hilaly and Sikdar (1994) was based on the concept of pollution balance. Currently, the WAR algorithm is one of the most practical methodologies for assessing the environmental impact of a process, especially at the conceptual design step. It allows assessing and com­paring the environmental friendliness of many different industrial processes. The methodology of the WAR algorithm developed by the National Risk Management Research Laboratory of the U. S. Environmental Protection Agency (EPA) uses the concept of potential environmental impact (PEI) and proposes to add a con­servation relationship over the PEI based on the input and output impact flows of the process. In this context, the PEI of a given quantity of mass and energy is understood as the effect that this mass and energy would have on average on the environment if they were to be discharged into the environment from this process (Cardona et al., 2004; Young and Cabezas, 1999). This definition implies that the impact is a quantity still not realized and the PEI has a probabilistic nature. Thus, the PEI of a chemical process is usually caused by the mass and energy that this process acquires or emits into the environment. In this way, the WAR algorithm is a tool to perform the evaluation of the PEI of a process based on the product streams, outlet wastes, and the feed to the process.

The overall PEI of a chemical k is determined by summing the specific PEI of the chemical over all the possible impact categories:

¥ k =^a‘ ¥ ш (10.1)

і

where ykl represents every impact category and al is the weighting factor, which is used to emphasize the particular areas of concern. These categories fall into two general areas of concern— global atmospheric and local toxicological—with four categories in each area. The four global atmospheric impact categories include

1. Global warming potential (GWP)

2. Ozone depletion potential (ODP)

3. Acidification or acid-rain potential (AP)

4. Photochemical oxidation or smog formation potential (PCOP)

The four local toxicological impact categories include

1. Human toxicity potential by ingestion (HTPI)

2. Human toxicity potential by either inhalation or dermal exposure (HTPE)

3. Aquatic toxicity potential (ATP)

4. Terrestrial toxicity potential (TTP; Cardona et al., 2004; Young and Cabezas, 1999)

This algorithm was modified in a previous work (Cardona et al., 2004) and the changes incorporated into the new versions of WARGUI (WAste Reduction algorithm—Graphical User Interface) software released by the EPA.

The WAR algorithm handles two classes of indexes to assess the environmen­tal impact of a chemical process. The first class measures the PEI emitted by the process while the other one measures the PEI generated within the process. Each class has two main indexes: total output rate of PEI (expressed as PEI per time unit) and PEI leaving the system per mass of product streams. The first class characterizes the PEI emitted by the system and is used to answer questions about the external environmental efficiency of the process, i. e., the ability of the plant to produce the desired products at a minimum discharge PEI. The second class of indexes characterizes the PEI generated by the system and its importance lies in the determination of the internal environmental efficiency of the process, i. e., how much PEI is being generated or consumed inside the process. The lower the values of these indexes, the more environmentally efficient the process is. Considering the variation of the plant capacities, the index per mass of products should be employed if the goal is to assess the environmental impact of a process independent of its production rate, especially if different alternative processes are to be evaluated (process synthesis).