Method for controlling automatic chemical dosing of high-turbidity water system

A control method and high turbidity technology, which is applied in the direction of flocculation/sedimentation water/sewage treatment, etc., can solve the problems of inability to obtain better sedimentation effect, dosing control, inability to adapt to changes in water quality of high turbidity water systems, etc.

Inactive Publication Date: 2013-04-03

CHONGQING WATER GROUP

5 Cites 54 Cited by

AI-Extracted Technical Summary

Problems solved by technology

Although FCD (Flower Imaging Analysis Feedback Control System), SCD (Flowing Charge Detection Feedback Control System), flow feedforward control and other automatic dosing controls a...

Method used

This control technology adopts neural network algorithm in conjunction with internal model PID control algorithm to optimize the control strategy, accurate dispensing, simulated manual dosing control process, can effectively overcome the unfavorable factors such as the raw water quality mutation of high turbidity water system. Adjusting PID and internal model PID to optimize t...

Abstract

A method for controlling the automatic chemical dosing of a high-turbidity water system trains a back propagation (BP) neural network according to the operation parameters of the flow, turbidity, temperature and pH value of historic source water, the outlet water turbidity of a sedimentation tank and the like, so that a neural network model is obtained, the parameters are then used as the input parameters of the BP neural network, an internal optimization algorithm of the BP neural network is utilized to obtain an output for guiding and controlling the chemical dosing amount, an internal model proportion integration differentiation (PID) algorithm is used to adjust the chemical dosing amount according to the turbidity of the sedimentation outlet water in real time, a flowmeter and a liquid level meter are utilized to accurately control the dosage concentration, the addition flow is calculated according to the dosage concentration and addition dose, PID control is carried out to regulate valve opening or dosing pump frequency according to the flow, and thus, the dosing flow is controlled. According to the method, the dose can be adjusted quickly according to the water source variation, the outlet water turbidity of the sedimentation tank can also be tracked automatically to appropriately correct, thus, the outlet water turbidity index of the sedimentation tank meets technological requirements, and the medicine consumption is also saved.

Application Domain

Water/sewage treatment by flocculation/precipitation

Technology Topic

Network modelValve opening +8

Image

Examples

Effect test(1)

Test Example

[0040] Applications:

[0041] This control method is used in the dosing control system of the Xuantan Temple water production workshop of Chongqing Jiangnan Water Plant. The raw water of the plant is Yangtze River water with a minimum annual turbidity of 20NTU and a maximum of 10,000NTU, of which more than 80 days are higher than 200NTU and more than 10 days high At 1000NTU, when the raw water turbidity is higher than 400NTU, the average rate of change of the sampled value is 450NTU/min. Before using the automatic dosing system, manual dosing is mainly used. In the high turbidity period, to ensure that the water quality is qualified, a large number of manual inspections and adjustments are required. The adjustment is very difficult due to the visibility at night. After adopting this control method, labor intensity is greatly reduced, and system safety, reliability and stability are improved. The effluent of the sedimentation tank is less than 5NTU, which fully meets the production needs. See the application effect Picture 8 , Picture 9 with Picture 10.

[0042] Advantages and positive effects of inventions or utility models

[0043] This control technology uses neural network algorithm combined with internal model PID control algorithm to optimize the control strategy, accurately dispenses medicines, and simulates the artificial dosing control process, which can effectively overcome the impact of unfavorable factors such as sudden changes in raw water quality in high-turbidity water systems. Adjusting PID and internal model PID to optimize the system, combined with periodic neural network training, can not only adjust the dynamic characteristics of the system in time, but also strengthen the system's self-learning and self-improvement capabilities, so that the system can gradually achieve optimal control while stabilizing, and reduce water production Material consumption improves production efficiency.

PUM

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