Optimal Selection of FGD Technology Using ACA-Based Rough Set Theory and VIKOR

Sulfur in coal is converted to sulfur dioxide during combustion. SO₂ is a precursor to acid rain which is one of the most widespread forms of pollution worldwide. To minimize the adverse impacts of SO₂ emissions on the environment, many flue gas desulphurization (FGD) systems have been developed over the past few decades for the control and abatement of SO₂ emissions by coal-fired boilers of industrial processes and power plants. However, how to select the optimum FGD system is one of essential problems facing many decision-makers. Since traditional methods have limitations in disposing deficient and noisy data, the past studies yielded in objectivity and accuracy for evaluation. In this study, the hybrid system of rough sets based on ant colony algorithm (ACA-RSs) and VIKOR algorithm was employed to dispose the problem of optimal selection, in which rough sets were used for simplifying the process of by eliminating the redundant data and determining the attributes´ weighs. However, many studies have showed that attributes reduction which is the core problem in rough set theory research is NP problem. We employed ACA to solve this problem. Then, VIKOR algorithm is applied to solve the multi-criteria decision-making problem. At last the effectiveness of our approach was verified by testing the hybrid system with an example.