Application of fuzzy set theory to industrial pollution prevention: production system modeling and life cycle assessment

 This research describes a framework and case study application that merges fuzzy set methods, pollution prevention, and sustainable production concepts. There is a direct linkage between industrial pollution prevention, sustainability, and the solution of large-scale environmental problems. This linkage stems from the inherent desire for economic production, while at the same time protecting the environment from further degradation. The methodology combines systems analysis under imprecise conditions with a life cycle assessment method that is able to accept imprecise data. Analysis of systems under imprecise conditions is accomplished through analysis of process flow diagrams using fuzzy set techniques. Introduction of imprecision into life cycle assessment is accomplished by integration of fuzzy set approaches into a decision support system utilizing multiple criteria decision making. The framework is described and a case study application of an industrial parts cleaning system using an open top vapor degreaser is presented. Results of applying the method show that: (1) It is well suited for analysis of complex systems in which input data is sparse and expensive to collect. (2) The proposed framework includes a decision support system that is able to consider life cycle assessment concepts, and is able to reconcile differing opinions on available options for modification of production systems, thereby leading to more sustainable solutions. The authors would like to acknowledge the USEPA Region VII Pollution Prevention Division, and the Pollution Prevention Incentives for States program, for their support of this research. In addition, the authors would like to recognize the support of the University of Nebraska Water Resources Center and Center for Infrastructure Research. The authors would also like to thank Dan Crist and Yuezhang Wang for their assistance with fuzzy systems modeling.