Fuzzy possibilistic modeling and sensitivity analysis for optimal fuel gas scheduling in refinery

In refinery, fuel gas which is continuously generated during the production process is one of the most important energy sources. Optimal scheduling of fuel gas system helps the refinery to achieve energy cost reduction and cleaner production. However, imprecise natures in the system, such as prediction of production rate of fuel gas, prediction of energy demand of the equipments and cost coefficient in the objective function, make the deterministic optimization method which requires well-defined and precise data cannot be competent for the fuel gas scheduling problem. In this study, fuzzy possibilistic programming (FPP) method is proposed to deal with these imprecise natures by triangular possibility distributions. The fuzzy possibilistic model is transformed into usual mathematical model by definition of necessity measure and the α-level method. Although FPP models have been widely applied to modeling, few research works have been reported on the performance evaluation, namely sensitivity analysis, of these models. Marginal value analysis, which is always used to provide additional economic information, is proposed to give the sensitivity analysis in the paper. This method is demonstrated to be much more flexible than the simulation method. Particularly, the analytical method is adopted to examine how the imprecise natures in the fuel gas system affect the scheduling results.