There are three spheres of sustainability: economic development, environmental stewardship and societal equity (Azapagic and Perdan, 2000; Sikdar, 2003a and 2003b; Heinzle et al., 2006). This is often touted as the “triple bottom line”. Economic indicators measure the profitability of a chemical process. These usually are the first criteria for companies; if the process is not economically feasible, the project would be aborted. Environmental indicators are concerned with efficient use of raw materials and energy in the process as well as the environmental impacts caused by emissions. The latter include human toxicology, ecotoxicity, global warming, ozone depletion, acidification, eutrophication and photochemical oxidation (Young and Cabezas, 1999; Jolliet et al., 2003; Bare et al., 2006). Societal indicators measure different aspects of working conditions and regulations. As these indicators are based on company and governmental policies, it is not appropriate to include them in the analysis and optimization of a chemical process.
In the literature, studies which considered process optimization for sustainability can be divided into two classes. The first comprises of studies where the various aspects of sustainability have been aggregated into a single indicator for single objective optimization (e.g., Dantus and High, 1999; Chen et al., 2002; Ramzan and Witt, 2006). The use of weighted method or analytical hierarchy process (AHP) to aggregate the indicators has their drawbacks; the most important drawback is its disability to find the non-convex portion of the Pareto optimal solutions (Deb, 2001). This calls for the use of multi-objective optimization. The second class comprises of studies that have performed multi-objective optimization on chemical processes (e.g., Lim et al., 1999; Kheawhom and Hirao, 2002; Hoffmann et al., 2004; Hugo and Pistikpoulos, 2005; Flores et al., 2007). These studies, however, have either chosen one environment impact category or a combined environmental index, and then optimized it along with an economic indicator (i.e. bi-objective optimization). There are many contributing factors for the environmental aspect as mentioned above. Minimizing the weighted sum of the various factors does not necessarily minimize the contribution of each individual factor. Hence, it would be more appropriate to categorize various impacts factors into groups and minimize the impact groups individually. An example of impact factor categorization is where ‘human toxicity by ingestion' and ‘human toxicity by inhalation' are grouped as ‘impact on humans'. This leads to a number of environmental impact groups. Optimization can be carried out with some/all environmental impact groups and with economic objectives simultaneously. Hence, for the first time, multi-objective optimization would be employed to optimize a basket of environmental and economic indicators, and not just two.
Two case studies have been chosen for the present study; they are a VOC (volatile organic component) recovery system (Chen et al., 2003) and a solvent recovery system (Chakraborty and Linninger, 2002). Six groups were identified for environmental impacts: (1) impact on humans, (2) impact on ecosystem – terrestrial and aquatic, (3) impact on local/global temperatures – global warming and ozone depletion, (4) photochemical oxidation, (5) acid rain, and (6) eutrophication. In addition, economic aspects would be considered in the optimization. Potential economic criteria are profit before taxes (PBT), payback period (PBP) and net present worth (NPW). Some or all these objectives will be optimized simultaneously using a multi-objective optimization tool, namely, non-dominated sorting genetic algorithm (NSGA-II), which was successfully used for many chemical engineering applications (Masuduzzaman and Rangaiah, 2008). As the objectives may be partially or totally conflicting, Pareto optimal solutions will be obtained. This will elucidate the trade-offs present and the decision maker would be better equipped in choosing the best solution. Net flow method and/or rough set method could be used as a tool in identifying the best Pareto optimal solution, whereby the decision maker's preference has to be declared (Thibault, 2008). While the choice of a solution may be subjective, the generation of the Pareto optimal solutions provides a quantitative foundation in reducing the biasness of the decision maker (Deb, 2001). Pareto optimal solutions and the best Pareto optimal solution for the two case studies will be presented and discussed. Insights gained from considering several objectives instead of just two will be highlighted.
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