Improving intelligence and efficiency of salt lake production by applying a decision support system based on IOT for brine pump management

Abstract

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. 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.