204. Application of DE-Based SVMs for Fouling Prediction on Thermal Power Plant Condensers

Unexpected fouling in condensers has always been one of the main operational concerns in thermal power plants. This paper describes an approach to predict fouling deposits in thermal power plant condensers by means of support vector machines (SVMs). The periodic fouling formation process and residual fouling phenomenon are analyzed. To improve the generalization performance of SVMs, an improved differential evolution algorithm is introduced to optimize the SVMs parameters. The prediction model based on optimized SVMs is used in a case study of a 300 MW thermal power station. The experiment results show that the proposed approach has more accurate prediction results and better dynamic self-adaptive ability to the condenser operating conditions change than asymptotic model and T–S fuzzy model.