Hunan University of Technology and Business

Linfeng Jin

Yang Chen

Changqing Su

Jiayin Guo

Huan Li

Liang Chen

Shengbo Gu

Xintong Ouyang

Xinxing Li

Hong Xue

Rongyuan Chen

Yongmei Xu

Jingling Yang

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.

Method, device, and medium for predicting flue dust concentration

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

Layout optimization method of water quality monitoring points based on rf-c-som clustering algorithm

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Changsha, China

Linfeng Jin