During the last decade, there has been a growing awareness of the importance of incorporating environmental concerns along with traditional economic criteria in the optimization of industrial processes. This trend has been motivated by several issues, a major one being the tighter government's regulations. Additionally, the need to improve the perception of the firm by customers for being more environmentally conscious, which may eventually lead to higher product sales, has also contributed to this business trend.

Particularly, nowadays a high percent of the total human-originated environmental impact is energy related [1]. For this reason, there has been extensive research on the area of design and planning of efficient energy systems considering both financial and environmental objectives [2,3]. A typical energy production system consists of (1) storage tanks to store raw materials, (2) boilers that convert fuel into steam at high pressure, (3) turbines that expand higher pressure steam into lower pressure steam in order to generate electricity and (4) mixing equipments for mixing compatible materials originated from different sources in the system. The goal of the environmentally conscious modeling tools that address the optimization of these systems is to reduce their environmental impact without having to sacrifice the economic performance of the process.

Unfortunately, despite the effort made so far, the majority of the strategies devised so far focused on the manufacturing stage, and for this reason their scope is rather limited. Because of this, they can sometimes lead to solutions that decrease the impact locally at the expense of increasing certain negative effects in other stages of the life cycle of the process, in such a manner that the overall environmental damage is increased. This drawback can be overcome by expanding the boundaries of the study beyond the production stage in order to include a wider range of production and logistic activities. Thus, in the past decade it has become clear that the environmental issues must be considered throughout the entire production chain [4].

With the aim to expand the scope of the modeling strategies for energy systems, this work presents a novel approach that relies on the combined use of multi-objective optimization and life cycle assessment (LCA) [5]. In our framework, the design and planning of energy systems is formulated as a multi-objective mixed integer lineal problem (mo-MILP) that simultaneously accounts for the minimization of cost and environmental impact. The latter objective is explicitly captured within the model by making use of the Eco-indicator 99 [6], which incorporates the recent advances made in LCA. The solution of the proposed formulation, which can be obtained via standard techniques for multi-objective optimization, provides as output the set of Pareto points that represent the optimal trade-off between the criteria considered in the analysis.

The capabilities of the proposed modeling framework and solution strategy are illustrated through a case study based on a real industrial scenario. In this context, our model is used to investigate a number of alternatives to produce and deliver electricity. The environmental data for the case study was retrieved from the Eco-invent database [7], which contains information regarding the main emissions associated with a wide range of industrial processes used in Europe. In this case study, it is clearly shown how the solely minimization of the environmental impact measured within the boundaries of the system leads to solutions where the environmental problem is just transferred to other stages of the energy supply chain (i.e., raw materials extraction, generation of utilities, etc.). The obtained results demonstrate the benefits of using a holistic environmental assessment method that covers the entire production chain (i.e., LCA), starting from the extraction of raw materials and ending with the delivery of energy to the final customers. The proposed method is intended to guide the decision-makers towards the adoption of more sustainable production patterns for energy generation, thus leading to a reduction of the overall impact caused to the environment.

References:

[1] EIA, Annual Energy Review 2005, Energy Information Administration, Report No. DOE/EIA-0384, 2005.

[2] Türkay, M., Oruç, C., Fujita, K., Asakura, T. 2004. Computers and Chemical Engineering, 28, 985-992.

[3] Solyu, A., Oruç, C., Türkay, M., Fujita, K., Asakura, T. 2006. European Journal of Operations Research, 174(1), 387-403.

[4] Srivastava, S. K. 2007. International journal of management reviews, 9(1), 53-80.

[5] Guinée, J. B.; Gorrée, M.; Heijungs, R.; Huppes, G.; Kleijn, R.; de Koning, A.; van Oers, L.; Sleeswijk, A. W.; S. Suh, S.; de Haes, H. A. U.; de Bruijn, H.; van Duin, R.;Huijbregts, M. A. J. Handbook on Life Cycle Assessment. Operational Guide to the ISO Standards; Kluwer Academic Publishers: Dordrecht, 2002.

[6] PRé-Consultants, "The Eco-indicator 99, A damage oriented method for life cycle impact assessment. Methodology Report and Manual for Designers", Technical Report, PRé Consultants, Amersfoort, The Netherlands, 2000.

[7] PRé-Consultants, SimaPro 6 LCA software; The Netherlands (www.pre.nl/simapro/default.htm), 1998.