Research On The Decision Support System For Automated Brine Extraction In Salt Lake Chemical Industry

In recent years,with the development of the saline lake chemical industry,the demand of brine resources keeps growing,so the enterprise carry out excessive exploitation of brine resources,resulting in insufficient supply of brine and resources starvation,affecting the sustainable development of the saline lake chemical industry.At the same time,because the equipment operation state is not stable,the equipment operation information owned by the staff is lack of effectiveness,and the staff adopts the traditional empirical analysis method to make the decision according to the information obtained by the manual inspection method,therefore the equipment failure rate and loss rate are high,which affects the stable exploitation and normal production of brine.In this context,the reasonable and stable mining of saline lake brine has become a serious problem.Therefore,in order to ensure the reasonable and stable exploitation of saline lake brine resources,the scientific and rational decision-making mechanism of brine mining is the key.Based on the theory of decision support system,this paper designs an effective solution that designs a automated decision support system for the brine mining of saline lake chemical industry.It mainly includes the following research work.Firstly,based on the analysis of the status of brine collection in the brine system and the actual production demand of the well mining,this paper designs a automated decision support system for the brine mining of saline lake chemical industry based on the theory of cloud computing and intelligent decision support system.And this paper designs the system from the system demand analysis,functional structure design,architecture construction,database design and other aspects.Then,this paper analyzes the characteristics trend of the typical failure of the brine pump.In view of the problems existing in the fault diagnosis of the pump and the advantages of the fault diagnosis method,this paper selects the appropriate analysis objects and research methods,analyzes the specific operation steps and the basis for parameter selection of the wavelet packet entropy feature extraction and support vector machine,and constructs the pump fault diagnosis model and the pump fault diagnosis system according to the related theory.Finally,this paper analyzes the development tools and environment of the system,shows the implementation of the system function interface,and tests the system with the 2016 full year data of a saline lake chemical enterprise.The test results show that the system can effectively diagnose the failure of the brine pump,and the overall diagnosis accuracy can reach 89.17%,which verifies the feasibility andreliability of the whole system.The establishment of the system achieves the automatic safe production of the saline lake wells,improves automation management level of production in the brine system.And the establishment of the system provides a scientific and accurate analysis and decision support for staff,improves the scientific and technological content of the decision,solves the problem of backward decision-making status in saline lake chemical enterprise,which makes the decisions of the chemical industry is more scientific and effective.Finally,the establishment of the system realizes the reasonable and stable exploitation of the brine resources of saline lake,increases the production efficiency of the enterprise,shows that the system has practical significance.