Shengbo Gu

Title: Innovations of Shengbo Gu: Pioneering Water Quality and Emission Prediction Technologies

Introduction

Shengbo Gu is an accomplished inventor based in Beijing, China. He has made significant contributions to the fields of environmental monitoring and emission prediction. With a focus on innovative methodologies, Gu has developed solutions that address critical challenges in these areas.

Latest Patents

Gu holds 2 patents that showcase his expertise. His first patent is titled "Method, device, and medium for predicting flue dust concentration." This invention discloses a method, device, and medium for predicting flue dust concentration. It calculates the flue dust emission amount of each batch of coal fed into a furnace based on hourly coal consumption. The invention generates a general rule between the data of coal fed into the furnace and the corresponding flue dust emission amount through training a prediction model. It accurately identifies the relationship between material and flue dust emission, reducing workloads of manual accounting and verification. Additionally, it provides a reference for Continuous Emission Monitoring Systems (CEMS) flue dust monitoring data. The use of an Adam algorithm to optimize a Back Propagation Neural Network (BPNN) allows for automatic adjustment of the learning rate for each parameter, enabling fast and efficient training of the prediction model. This invention effectively addresses measurement errors and complex manual accounting, achieving precise measurement of flue dust emissions from coal-fired power plants.

His second patent is titled "Layout optimization method of water quality monitoring points based on RF-C-SOM clustering algorithm." This method includes preprocessing collected water quality data to obtain preprocessed data used for training a random forest model. The model determines the feature importance of water quality indicators. Important features are selected based on feature importance and model training accuracy. The method performs dimensionality reduction on the preprocessed data and conducts fuzzy clustering to classify water quality sections. It initializes neurons and trains a self-organizing mapping network model, obtaining a point clustering result. A water quality index evaluation is conducted for the clustering result before and after screening.

Career Highlights

Shengbo Gu is affiliated with Hunan University of Technology and Business, where he contributes to research and development in environmental technologies. His work has garnered attention for its practical applications in improving water quality monitoring and emission prediction.

Collaborations

Gu collaborates with notable colleagues, including Huan Li and Liang Chen, who contribute to his