Method, device, and medium for predicting flue dust concentration

Abstract

The invention discloses a method, a device and a medium for predicting a flue dust concentration, which calculate a flue dust emission amount of each batch of coal fed into a furnace based on hourly coal consumption of a unit as a label value of a prediction model, generate a general rule between data of the coal fed into the furnace and a corresponding flue dust emission amount through training the prediction model, accurately identify a relationship between material and the flue dust emission amount, reduce workloads of manual accounting and verification, and provide a reference for CEMS flue dust monitoring data. At the same time, using an Adam algorithm to optimize a BPNN allows for automatic adjustment of a learning rate for each parameter, enabling fast and efficient training of the prediction model. The invention can solve problems of measurement errors and complex manual accounting and verification in the related art, thereby achieving precise measurement of the flue dust emissions from coal-fired power plants and reducing workloads of manual operations.