Optimization of Flue Gas Desulphurization Technologies Based on Cloud Model and Kernel Vector Space Model

• Main Authors: Yiyi Zhang, Yang Li, Jiefeng Liu, Hanbo Zheng, Chaohai Zhang, Ruqi Li, Haoyi Su, Junhui Zhao
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Flue gas desulphurization; kernel vector space; cloud model; proximity analysis; comprehensive evaluation
• Online Access: https://ieeexplore.ieee.org/document/8747348/

The flue gas desulfurization of coal-fired power plants is currently the main mean to control the emission of sulfur dioxide in China. How to select desulfurization technology that meets both technical and economic requirements becomes the issue of this paper. To deal with problems in evaluating indexes with linguistic evaluation information in the optimization of flue gas desulphurization technologies, a new synthetic evaluation model for flue gas desulphurization technologies based on the cloud model and the kernel vector space method was proposed in this paper. The main contribution of this paper is as follows. First, the comprehensive evaluation index system includes technology, economy, and environmental performance. The technology indicators are qualitative index; economic and environmental indicators belong to quantitative index. In this case, the reasonable transformation from qualitative concepts to quantitative indication was accomplished and the cloud model was used to represent the natural language evaluation information. Second, the subjective weights were determined by the analytic hierarchy process and the objective weights were determined by the entropy method so that the comprehensive weights can be obtained by the method of combining the additional principle with both subjective and objective weights' information. Finally, the priority membership of evaluation objects with the kernel vector space theory was calculated to achieve the optimization of flue gas desulphurization technologies. By comparing and analyzing six different representative flue gas desulphurization technologies, it can be proved that the model has advantages of objectivity, simplicity, effectiveness, and implementation simplicity, and can provide a reliable basis for making a decision in the optimization of flue gas desulphurization technologies.