An improved online learning algorithm and its applications on leak points prediction of gas pipe in petrochemical industries

In petrochemical industries, one of the most concerned problems is the leaking of toxic gas. Once leaking occurs, the safety of equipments located in production site is greatly threatened, thereby affecting surrounding environment. In order to solve this problem, it is necessary to predict the possible location of leak points from sensors which are located in gas pipe. On the other hand, data from sensors of petrochemical industries need to be timely operated because of time sensitivity, and it is hard to achieve associated information from sensors located in production site. To this end, an OLA-IBP (Online Learning Algorithm based on Improved Back Propagation) is proposed. The adaptive structure of this algorithm is settled online. Meanwhile, real-time data streams are parallelly processed according to arriving time in input layer. Simulation results show that OLA-IBP can efficiently improve learning time and accuracy rate. Finally, the adaptability of OLA-IBP is verified in leak points prediction of petrochemical equipments from processed data.