Soft Sensor Modeling Method for Sulfur Recovery Process Based on Long Short-Term Memory Artificial Neural Network (LSTM)

Abstract

In the process of sulfur recovery, H2S and SO2 concentrations reflect the effectiveness and reliability of the recovery process. However, the concentration of H2S and SO2 in the process of sulfur recovery is difficult to be measured by online analysis instrument, so the soft sensing modeling method is often used to analyze the system. Because SRU system has strong nonlinear characteristics and dynamic process characteristics,traditional soft sensing modeling methods are often limited in use. Long Short-Term Memory (LSTM)Artificial neural networks show strong ability in processing nonlinear data and dynamic data. Therefore, LSTM soft sensing method is used in this paper to systematically analyze the sulfur recovery process.