Huan Li

Title: Innovations by Huan Li: Advancements in Environmental Monitoring

Introduction

Huan Li is an accomplished inventor based in Changsha, China. He has made significant contributions to the field of environmental monitoring through his innovative patents. With a focus on improving measurement accuracy and reducing manual workloads, his work is vital for the advancement of technology in this area.

Latest Patents

Huan Li holds two notable patents. The first patent is titled "Method, device, and medium for predicting flue dust concentration." This invention discloses a method, a device, and a 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 the 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. This provides a reference for Continuous Emission Monitoring Systems (CEMS) flue dust monitoring data. Additionally, 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 addresses measurement errors and complex manual accounting, achieving precise measurement of flue dust emissions from coal-fired power plants.

The 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 as input. It employs water quality categories as labels to train a random forest model to determine the feature importance of water quality indicators. Important features are selected based on feature importance and model training accuracy. Dimensionality reduction is performed on the preprocessed data to obtain dimension-reduced data. A fuzzy clustering is then conducted on this data to classify water quality sections. Initial weight values for a self-organizing mapping algorithm are determined, and a self-organizing mapping network model is trained. The model yields a point clustering result, followed by a water quality index evaluation for the clustering result before and after screening.

Career Highlights

Huan Li is affiliated with Hunan University of Technology and Business, where he contributes to research and development in environmental technology. His work is instrumental in enhancing the accuracy of environmental monitoring systems.

Collabor