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Electronics 2018, 7(8), 147; https://doi.org/10.3390/electronics7080147

Improving Intelligence and Efficiency of Salt Lake Production by Applying a Decision Support System Based on IOT for Brine Pump Management

1
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2
College of Engineering, China Agricultural University, Beijing 100083, China
3
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
*
Authors to whom correspondence should be addressed.
Received: 20 July 2018 / Revised: 9 August 2018 / Accepted: 10 August 2018 / Published: 14 August 2018
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Abstract

At present, due to their geographical distribution, environmental conditions and traditional monitoring technologies, the manual inspection of brine pumps in Qinghai Saline Lake can not be effectively carried out in real time, so the pumps have a high failure rate. This has seriously affected the chemical production of this saline lake. The paper designed a remote real-time monitoring terminal and a decision support system based on LoRa technology, GPRS (General Packet Radio Services) remote communication technology and remote-control technology. The system integrated the liquid-level sensing model and the decision support model for brine pump management. The system monitored and analyzed the voltage, current, and liquid-level parameters in real time to determine the operating status or failure of the brine pump. The ID3 (Iterative Dichotomiser 3) method was used to establish the correlation models between the dynamic monitoring information and the brine pump failure, which is the core of the decision support model. The remote controller was implemented to display and control the running status of the brine pumps when the maintenance personnel received the warning information. PHP (Hypertext Preprocessor) language and a MySQL database were implemented to realize the data display, management and decision support system. View Full-Text
Keywords: salt lake brine mining; LoRa wireless sensor network; remote real time monitoring; decision support model; remote control salt lake brine mining; LoRa wireless sensor network; remote real time monitoring; decision support model; remote control
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Cui, Y.; Liu, H.; Zhang, M.; Stankovski, S.; Feng, J.; Zhang, X. Improving Intelligence and Efficiency of Salt Lake Production by Applying a Decision Support System Based on IOT for Brine Pump Management. Electronics 2018, 7, 147.

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