Development of a new multi-layer perceptron based soft sensor for SO2 emissions in power plant

Abstract

In the paper, a multi-layer perceptron (MLP) based soft sensor for SO2 emission in desulfurization process of thermal power plants is proposed. Firstly, the production process and variables involved in the desulfurization system of thermal power plants are analyzed. Secondly, the MLP network and its input variable selection algorithm with nonnegative garrote (NNG) and extremal optimization (EO) are studied. The proposed algorithm employs MLP to model the complex desulfurization process, and then conducts shrinkage on input weights of MLP by NNG. After that, further local variable selection is performed by EO and the final model is presented. Thirdly, the simulation on actual production data of a power plant and comparisons with other state-of-art soft sensors are made to demonstrate the performance of the proposed algorithm. The simulation results show that the proposed algorithm can accurately predict the target variable and has superior performance to other algorithms. Moreover, the variable importance analysis with our approaches are consistent with the field operating experience and can provide reference for the further optimization of the control system of desulfurization process.