Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants

Abstract

This study develops an adaptive selective catalytic reduction (SCR) model in a coal-fired power plant with typical operating data to tackle two issues: selecting appropriate samples for model training and maintaining model accuracy under new operating conditions. First, an index of representing the information contained in the operating data of SCR is defined by considering three factors including variation span, distribution status, and information redundancy. Next, the genetic algorithm (GA) is applied to select typical operating data from SCR operational database by maximizing the information index. These data are taken as the training set to develop SCR models and predict NOx emissions with artificial intelligence techniques, including least square support vector machine and artificial neural network. Furthermore, typical operating data are managed adaptively to cover information from new operating conditions, and SCR models are updated according to the data change. SCR models trained with data from other common selections are compared. Results show that the typical operating data selected by GA can contain large information, and the developed models perform better than those trained with data from other selections. In addition, data management and model update can make the model maintain high prediction accuracy when new operating conditions occur.