Research on the Early-warning Model of Foaming Phenomenon in Desulfurization Solution System of Natural Gas Purification Plant Based on Artificial Intelligence Technology

摘要

In order to ensure the safety of industrial production process, identification and early-warning of abnormal working conditions are very important. In this paper, the abnormal "foaming phenomenon" of natural gas purification desulfurization solution system is taken as the research object. The identification of the current abnormal "foaming phenomenon" mainly relies on the traditional method of long-term manual judgment of field technicians, which consumes a lot of resources and is easy to cause negligence. The model is based on the real-time online operation data of the 300W/d desulfurization system of the purification plant. The artificial intelligence technology is used to model the abnormal "foaming events", and the automatic identification and early warning of such events is achieved. The experimental results prove that the accuracy of the early-warning model can reach 97%, and the early warning results have been affirmed by professionals. At the same time, on the basis of the successful early-warning model, it is integrated into the "safety and environmental protection early warning visual management system of the oil and gas production". The real-time trend of key data dimensions and the probability of abnormal foaming have been well performed. Real-time warning function of abnormal "foaming phenomenon" of the 300W/d equipment of the purification plant is realized.