NOx Concentration Prediction Based on Sparrow Algorithm Optimized Support Vector Machine
As the main energy source for power generation in thermal power plants,coal generates large amounts of NOx during combustion in the boiler.Various power plants generally use automatic flue gas monitoring systems to measure their concentrations in real-time,but due to the large delay in measurement,they cannot accurately reflect the real-time changes in NOx concentration at the SCR system.This paper proposes a NOx concentration prediction method based on an improved sparrow algorithm optimized by least squares support vector machines.Firstly,the cosine factor is introduced to improve the proportional operator in the sparrow algorithm,and the information on the number of iterations is introduced into the iterative process to balance the global and local search ability in the first and second stages of the algorithm.Secondly,a new variational operator is used instead of the original operator,and chaos theory is integrated into the sparrow algorithm to solve the problems of poor global search ability,unstable distri-bution of initialized sparrows and insufficient ways to update the location of discoverers.Finally,the improved sparrow algorithm(CDE-SSA)is used to perform parameter search for the least squares support vector machine(LSSVM).Experimental results demonstrate that the method shows good performance in terms of both accuracy and stability of NOx concentration prediction.
Sparrow algorithmLeast squares support vector machineNOx concentrationThermal power unitPrediction model
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