Abstract
The NOx emission is one of the major air pollutants of thermal power plants. Due to the complexity of the boiler production process, the NOx emission was related with many parameters (such as, primary air flow, fuel flow). Building a NOx emission prediction is the basis of combustion optimization and emission reduction. A least square support vector machine (LSSVM) model was developed and verified. Good prediction performance was achieved with the proper learning parameters which were chosen by a differential evolution (DE) algorithm. To illustrate the effectiveness of the DELSSVM, BP neural networks, Multi-layer perceptron (MLP) and partial least squares (PLS) regression algorithm were employed to make a comparison. Experimental results show that the DE optimized LSSVM (DELSSVM) has a promising performance in the prediction of the NOx emission.
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