A novel MINLP-based representation of the original complex model for predicting gasoline emissions

The Environmental Protection Agency introduced Reformulated Gasoline (RFG) requirements as a measure to reduce emissions from gasoline-powered vehicles in certain geographic areas. The EPA developed models for predicting emissions as a function of gasoline properties and established statutory baseline emissions from a representative set of gasolines. RFG is a key driving force for investment in the North American refining industry. All reformulated gasoline requires certification via the complex model, and all refiners and importers calculate emissions performance reductions from the statutory baseline gasoline. The RFG models introduce increasing complex constraints with the major limitation that it is implicitly defined through a series of complicated disjunctions assembled in the form of spreadsheets by the EPA. This implicit and cumbersome representation of the emissions predictive models renders rigorous optimization and sensitivity analysis very difficult. In this paper, we discuss how the federal government requirements for reformulated gasoline can be restated as a set of mixedinteger nonlinear programming (MINLP) constraints with the aid of disjunctive programming techniques.