China Institute of Water Resources and Hydropower Research

China Three Gorges Corporation

Yongnan Zhu

Hao Wang

Chao Wang

Huichao Dai

Zhao Zhang

Zhaohui Yang

Heng Yang

Xiaohui Lei

Fan He

Yong Wang

Yong Zhao

Shan Jiang

Jiaqi Li

Jianhua Wang

Lizhen Wang

Haihong Li

Qingming Wang

Changhai Qin

Jiaqi Zhai

Guohua He

Lingzhong Kong

A method for predicting and controlling a water level of a series water conveyance canal on the basis of a fuzzy neural network is disclosed. The method includes: performing the relationship between a sluice opening degree and an open canal control water level by means of a fuzzy neural network, and constructing an upstream water level controller of a coupled predictive control algorithm; solving an optimal control rate of the upstream water level controller using a gradient optimization algorithm on the basis of a control target of the upstream water level controller; and generating a control strategy by collecting actually measured water level change information and multiplying the actually measured water level change information by the optimal control rate on the basis of the solved optimal control rate, thereby fulfilling the object of predicting and controlling the water level.

Method for predicting and controlling awater level of a series water conveyance canal on a basis of a fuzzy neural network

A high-efficiency solar multi-layer evaporative seawater desalination device includes a transparent housing. In the device, wedge-shaped recesses are formed on an outer wall of the transparent housing; during seawater desalination, seawater is injected into evaporation trays and sunlight is irradiated on the evaporation trays through the transparent housing; the transparent housing is made of a transparent plastic material, such that sunlight can directly enter the transparent housing and increase a temperature therein to speed up the evaporation; the wedge-shaped recesses are on a shady surface; during desalination, seawater in the evaporation trays absorbs heat and evaporates to form water vapor, such that there is a given temperature difference between the water vapor and the outside; and a lower part of the wedge-shaped recess can serve as a condensation surface, the water vapor condenses on the transparent housing or at the wedge-shaped recess.

High-efficiency solar multi-layer evaporative seawater desalination device

A real-time abnormity-diagnosing and interpolation method for water regime-monitoring data relates to the technical field of monitoring water regime. This method includes the following steps: acquiring water regime-monitoring data, drawing a box plot, recognizing and diagnosing abnormal data in real time based on the box plot, performing grey correlation analysis on other variables related to a predictor variable, building a BP neural network model and making training, applying the BP neural network model to predict water regime-monitoring data in real time, and performing abnormity diagnosis and data interpolation. Adopting this method, we can effectively enhance predicting and monitoring the water regime-monitoring data in real time, and diagnose abnormal data and make interpolations in time, thereby improving the reliability of data, objectively reflecting water regime changes, and effectively guiding engineering scheduling.

Real-time abnormity-diagnosing and interpolation method for water regime-monitoring data

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Beijing, China

Yongnan Zhu